Adventures in Automation

Need a speaker at your Software Testing Meetup? How about a talk about putting together a React Native mobile testing framework?

2026-05-29T20:22:04.090-04:00

Are any software testing Meetups looking for a speaker? I have a talk, all ready to go!

After surviving the first round of layoffs last December, I scrambled to organize my research notes on building a mobile automation framework into a presentation. I managed to finish it days before I was hit by the second round of layoffs at the end of this January.

So far, I have given my presentation, Building a React Mobile Automated Test Framework Using Detox + TypeScript to:

TestGuild.com Automation Guild 2026 via Zoom (4/6/2026)
Software Quality Group of New England ( SQGNE ) @ SCRUM.org, Burlington, MA - https://tinyurl.com/detox-demo-sqgne (5/20/2026)
Sydney Testers Meetup via Zoom (scheduled on 6/30/2026)

Two Questions:

If I give a talk to a software testing Meetup in Australia via Zoom, even though I never left my home office in Massachusetts, would I be considered an "International Speaker"?
Does anyone else want me to volunteer to speak at their software testing meetup? The SQGNE season is ending for the summer, so I will have some time available.

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Practicing Playwright: API Testing, Intercepting Network Requests, and Mocking APIs

2026-05-27T22:20:30.432-04:00

How would you intercept a network request and use the data for assertions? Mock a network request using those assertions? And make sure that all data is loaded in the UI before making your assertions, and that all tests can pass when run?

We will be walking through Butch Mayhew's code that answers all of these questions, part of his LinkedIn Learning course: Playwright Essential Training. We will be examining the shopping cart test site PracticeSoftwareTesting.com and looking at code on Butch's companion GitHub site on how to mock out the API and use them in the UI tests.

Playwright has a lot of features when it comes to API testing. You can intercept network requests, aborting, modifying, and mocking network requests. You can also simulate a slow network. This is all done through the Playwright methods: page.route.

A route is the specific path or URL a client uses to request data or trigger a function on a server. GET /product would be an API call that gets everything from the address string called products, which then retrieves all products from the API endpoint called "products". The endpoint is "products" and the route is the name that accesses the endpoint.

The Playwright API has a method called route, which allows Playwright to monitor and modify browser network traffic.

Playwright.dev / Docs / Network: https://playwright.dev/docs/network
Playwright and page.route: https://playwright.dev/docs/api/class-route
Playwright Mocking API Guide: https://playwright.dev/docs/mock

Adding page.route to your test allows you to do things such as:

abort the route request with route.abort(), simulating various error codes such as accessdenied, addressunreachable, blockedbyclient, blockedbyresponse, connection aborted, connectionclosed, timedout, etc.
continue sending the route's request with optional overrides such as changing the request URL, the headers, the method such as GET or POST, and the post data of the request.
fetch the request without yet fulfilling it, so the response can be saved, modified, and fulfilled later. You can also change the HTTP headers, follow redirects automatically, etc.
fulfill the route's request.
request that the request is routed.

About Our Site Under Test

PracticeSoftwareTesting.com is a shopping cart with a web-based front-end pulling data from a web-based back end. Tools can be filtered out by category, such as hand tools (hammer, hand saw, wrench), and power tools ( grinder, sander, saw ) and by brand, such as ForgeFlex Tools and MightyCraft Hardware. Check the appropriate check box, and cards are display in a grid fashion of products.

These products can also be sorted, such as by "Name (A - Z)" and ""Name (Z - A)"

Explore the HTML of the site by opening up Google Chrome, right clicking on the first product in the grid to "Inspect" to open Chrome Dev Tools and look at the Elements tab to find locators:

Product Grid: Class col-md-9

This class can be used as a locator to find the product grid.

About the Products API

Let's see an example product returned by the Products API Endpoint:

Go to https://practicesoftwaretesting.com/
Under the Filters category, check the checkbox "Saw" under "Power Tools". You will see one item display: a circular saw.

But where did the product information come from?

Under Chrome Developer Tools, select the "Network" tab
Uncheck and then check the "Saw" checkbox, and you will see a new products query at the bottom looking like products?page=page=0... select that.
Select the "Response" tab.

  
"current_page": 1,
    "data": [
        {
            "id": "01KSMVRWS80N7909CZTE7ACJQS",
            "name": "Circular Saw",
            "description": "Portable circular saw powered by a robust ...",
            "price": 80.19,
            "is_location_offer": false,
            "is_rental": false,
            "co2_rating": "D",
            "in_stock": true,
            "is_eco_friendly": false,
            ...
            "category": {
                "id": "01KSMVRWMQCBR9XDQ5TGC47MS6",
                "name": "Saw",
                "slug": "saw"
            },
            "brand": {
                "id": "01KSMVRW6HT9HQZ2E113FQEFX7",
                "name": "ForgeFlex Tools"
            }
        }

All this data was returned in a JSON format when the web app took the information in the filter and made the call to the product API

https://api.practicesoftwaretesting.com/products?page=0&between=price,1,100&by_category=01KSMVRWMQCBR9XDQ5TGC47MS6&is_rental=false

All these parameters can be used to test out how your UI behaves under various conditions.

How to Intercept Network Responses

We can intercept the information coming back from the API using route.fetch and then using route.continue:

await page.route(
  "https://api.practicesoftwaretesting.com/products**"
  async (route) => {
    const response = await route.fetch();
    products = await response.json();
    route.continue();
  }
};

A glob, or "global command", is a pattern of characters used to find files, folders, or web addresses. The "**" is a globbing wildcard that matches all the nested folders which come after it. Had Butch used only one "*" it would have only returned the next level in the folder hierarchy.

What does this code do?

await page.route: Tells Playwright to listen to network requests that match this URL pattern.
async (route): Creates a callback function that triggers each time a call is made.
const response: Declares a new constant variable called "response" where we will store the HTTP response retrieved.
await route.fetch(): Performs the actual network request and retrieve the reponse.
products = await response.json: Extracts the reponse payload as a JavaScript Object (JSON) and assigns it to a variable named "products".
route.continue: Proceed with the original request.

How to Abort API Requests

Why would we want to abort a certain network call? Butch mentions in his LinkedIn Learning class how a certain pop up was interfering with a UI test he was writing.

You can also use route.abort in order to simulate the Product API being down, and check to see that the UI is behaving as it should.

Sample code Butch listed:

await page.route(
  "https://api.practicesoftwaretesting.com/products**"
  (route) => route.abort()
};

With this code, we are seeking to intercept anything from the Products API, and when the match is found, cancel the network request before it ever reaches the server.

How to Mock a Product

Let's say we want to see how our UI behaves with product names that are quite long, prices with many digits, or if a product is in stock. We can add these test products to the database and then search for them, or we can:

Capture the response
Alter any of the fields returned
Submit that mock data as if coming from the API itself.



await page.route(
  "https://api.practicesoftwaretesting.com/products**"
  async (route) => {
    const response = await route.fetch();
    const json = await response.json();
    json.data[0]["name"] = "Mocked Product";
    json.data[0]["price"] = 100000.01;
    json.data[0]["in_stock"] = false;

    await route.fulfill({ response, json }  ))
  }
};

... For this, we are editing the name, price, and the variable to see if the item is in stock or out of stock.

json.data[0] represents the very first product listed.

How to Mock API Requests with HAR files

If you want to capture all HTTP traffic and store it, you can store it in an HTTP Archive Record, or HAR file.

According to Playwright.dev, a "HAR file is an HTTP Archive file that contains a record of all the network requests that are made when a page is loaded. It contains information about the request and response headers, cookies, content, timings, and more. You can use HAR files to mock network requests in your tests. You'll need to: 1) Record a HAR file. 2) Commit the HAR file alongside the tests. 3) Route requests using the saved HAR files in the tests.

"To record a HAR file we use page.routeFromHAR() or browserContext.routeFromHAR() method. This method takes in the path to the HAR file and an optional object of options. The options object can contain the URL so that only requests with the URL matching the specified glob pattern will be served from the HAR File. If not specified, all requests will be served from the HAR file.

"Setting update option to true will create or update the HAR file with the actual network information instead of serving the requests from the HAR file. Use it when creating a test to populate the HAR with real data".

await page.routeFromHAR(".hars/product.har", {
  url: "https://api.practicesoftwaretesting.com/products**"
  update: false,
});

With this code we are:

Telling Playwright to intercept all network traffic for the current page using a recorded HAR file and store it in the root directory folder we are calling ".hars" in the file "product.har".
Update: false runs the test in playback mode. We can run the test once with "update: true" to capture and store the information, then reset it to "update: false" to not have the data overwritten.

The problem with this approach is that sensitive data such as passwords might be part of the HAR file. To sanitize sensitive data, see the Medium article: HARmageddon is cancelled: how we taught Playwright to replay HAR with dynamic parameters

Simulating Slow API Requests

Let's say we want to see how the UI responds when it takes a good 4 seconds (4000 milliseconds) for the API to respond.

await page.route(
  "https://api.practicesoftwaretesting.com/products**"
  async (route) => {
    await page.waitForTimeout(4000)
    await route.continue();
  }
};

We can then add a bit of lag into the system, then continue as normal.

Write a Test: Validating Brands

Putting together Butch's lessons above, Butch had an exercise listed: How can you validate the brands listed in the Filter on the web site matches the ones listed in the brand endpoint? And how can you do it by intercepting network data?

If you go to https://practicesoftwaretesting.com/ you can see that under the Filters, there are two major brands: ForgeFlexTools and MightyCraft Hardware.

As mentioned earlier, these products can be sorted, such as by "Name (A - Z)" and ""Name (Z - A)"

Explore the HTML of the site by opening up Google Chrome, right clicking on the first product in the grid to "Inspect" to open Chrome Dev Tools and look at the Elements tab to find locators:

Product Grid: Class col-md-9

This class can be used as a locator to find the product grid.

We can get the information about the brands from the API, which Butch stored in the .env file as API_URL.

Using this information, we can write an end-to-end test to verify that the dynamic brand data loads correctly on a web page.

This test verifies that the web application displays a list of product brands on its front end by intercepting the backend API network responses.

home.spec.ts

test("validate brands by intercepting network data", async ({ page }) => {
  let brands: any;
  const apiUrl = process.env.API_URL;
  await test.step("intercept /brands", async () => {
    await page.route(apiUrl + "/brands", async (route) => {
      const response = await route.fetch();
      brands = await response.json();
      route.continue();
    });
  });
  await page.goto("/");

  const productGrid = page.locator(".col-md-9");
  await expect(productGrid).toBeVisible();
  await expect(page.locator(".skeleton").first()).not.toBeVisible();

  const brandFilterSection = page.getByText("SortName (A - Z)Name (Z - A)");

  for (const brand of brands) {
    await expect(brandFilterSection).toContainText(brand.name);
  }
});

tests/homepage/home.spec.ts

let brands: any: Declaring an empty variable to store all the brands found when they are fetched from the network. It is declared outside the test step so we can use it later on. It is set to "any" in order to keep it undefined for now.
const apiUrl gets the API URL from the .env folder.
await test.step("intercept /brands: This time, instead of products, we are registering a handler that fires whenever a request matches the URL pattern of "/brands".
route.fetch makes the real network request on behalf of the browser.
response.json parses and captures the response body into the variable, "brands".
route.continue forwards the original response to the browser unchanged, so the UI renders normally.

The UI is getting the real data, with the test getting a copy of it.

Then we are doing:

page.goto("/"): Loads the page and the browser makes the call, including GET /brands.
When the page loads and the browser calls GET /brands, the handler executes and the "brands" variable we set up is populated.
page.locator: Sets up the contant variable productGrid as a locator for the product grid.
We expect the product grid to be visible, showing that the page is now loaded, and that the initial skeleton element placeholders PracticeSoftwareTesting.com has is no longer visible.

Finally, we are validating the UI data against the API data we have captured:

We locate the Brand Filter Section web element by text, where we can sort the filter.
We then loop through every single brand object that was captured in the "brand" variable.
We confirm that each brand name provided in the backend API is accurately printed as text inside the frontend filter sidebar.

Write a Test: Validating Categories By Mocking

Let's say we want to write a test that the Categories API correctly adds mocked categories and subcategories to the left side index. Butch shows an example:

home.spec.ts

test("validate categories render in UI by mocking", async ({ page }) => {
  let categories: any;
  const apiUrl = process.env.API_URL;

  await test.step("intercept /categories", async () => {
    await page.route(apiUrl + "/categories/tree", async (route) => {
      const response = await route.fetch();
      const json = await response.json();
      categories = json.data;

      json[0].name = "Mocked Category";
      if (json[0].sub_categories && json[0].sub_categories.length > 0) {
        json[0].sub_categories[0].name = "Mocked Subcategory";
      }
      await route.fulfill({ response, json });
    });
  });
  await page.goto("/");

  const productGrid = page.locator(".col-md-9");
  await expect(productGrid).toBeVisible();
  await expect(page.locator(".skeleton").first()).not.toBeVisible();

  const categoryFilterSection = page.getByText("SortName (A - Z)Name (Z - A)");

  await expect(categoryFilterSection).toContainText("Mocked Category");
  await expect(categoryFilterSection).toContainText("Mocked Subcategory");
});

tests/homepage/home.spec.ts

Walking through this code:

Create a new test:

A new Playwright test case is defines, using a page fixture.
We declare the undefined variable "categories" and add the API URL into a constant variable.
We create a new test step, letting readers know we are going to intercept the /categories data.

Intercept the response:

With page.route(apiUrl + "/categories/tree" we tell the browser to watch for and pause any outgoing requests matching /categories/tree.
Route.fetch allows the real request to fetch the real response, and store it in the variable called "response".
We then take that response in json format and place it in a variable we are calling "json".

Edit that response so the first item has "Mocked Category" and "Mocked Subcategory":

We are going to take that first name, [0] and assign it the string "Mocked Category".
If there are subcategories in that intercepted response, and the sub_categories length is greater than zero, we will assign the string to it: "Mocked Subcategory".
route.fulfill will send this modified JSON response back to the frontend application instead of the original server data.
Hopefully, in the left hand side panel of PracticeSoftwareTesting.com, under "Category" we will see "Mocked Category" and "Mocked Subcategory".

Load the Page, Wait for the Product Grid to Appear:

We set up productGrid as a web element, using the class we found .col-md-9 as a locator.
We expect the productGrid to be visible and the .skeleton to not be shown.

Expect the "Mocked Category" to appear!

We declare a new web element, categoryFilterSection, using the SortName as a locator.
Can we verify that the phrase "Mocked Category" appears? It passes!
Can we verify that the phrase "Mocked subcategory" appears? It passes!

Whew! And this is just a taste on what Playwright can do to test at the API level. You can read more in the Playwright.dev Docs which covers API Testing, and the classes API Request, API Request Context, and API Response.

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Practicing Playwright: Visual Testing With Playwright

2026-05-21T08:46:54.520-04:00

If you want to do some basic visual checking to see if there has been any deviation from your baseline of images, you can use Playwright. It's built in! Playwright can take a snapshot of a web element, a visible viewport, or a full page, and save it in your Git repository as a baseline, failing the test if the image, page, or viewport does not match up.

Caution: From what I have been reading, this can quickly cause your code repository to balloon in size, since Chrome, Firefox, and WebKit would each store its own golden screenshot in your repo. Also, images on Mac, Windows, and Linux all appear different pixel-by-pixel. If using a CI platform, it might be best to run visual tests only on a standard Playwright Docker image, to generate and compare snapshots. According to TestQuality, "Once a suite passes 50–100 visual tests, teams need a layer that tracks run history, surfaces flaky-test patterns across cycles, and routes confirmed defects into the team's tracker — none of which lives inside the test runner itself"... I wonder if you can store images in an Amazon S3 bucket and hook that up as a virtual drive? ... no matter. That will be a blog post for another time...

Right now, we will be walking through Butch Mayhew's code he wrote for his LinkedIn Learning course, Learning Playwright, found on his companion GitHub site.

While the test is in a certain state you can a screenshot of the page or certain elements of the page and save them as a snapshot. The snapshots an be used as a baseline images to compare your current site against. This baseline can be periodically updated as the site evolves.

How does this happen? With Playwright's .toHaveScreenshot( ) to take a screenshot and the mask method that you want to leave out of the comparisons between the expected and actual screenshots.

toHaveScreenshot(name): Playwright.dev / Page Assertions

Our Test Site: PracticeSoftwareTesting.com

Butch has a series of tests for the test site Practice Software Testing ( practicesoftwaretesting.com ).

If you go to the site, you can see that there is an ever moving logo, "Toolshop Demo" which has the title "Practice Software Testing - Toolshop" . Let's write a test capturing an image of the page, leaving out the moving logo.

Butch also suggested waiting for everything to be fully loaded with "networkidle". But, according to the Playwright.dev waitForLoadState documentation, it says "Don't use this method for testing, rely on web assertions to assess readiness instead". Hrm. Let's go with that he has for now.

Writing the Visual Test

home.spec.ts

  test("visual test", async ({ page }) => {
    await page.waitForLoadState("networkidle");
    await expect(page).toHaveScreenshot("home-page-no-auth.png", {
      mask: [page.getByTitle("Practice Software Testing - Toolshop")],
    });
  });

tests / homepage / homepage.spec.ts

The first time you run this test, it fails, since there are not yet any snapshots saved. There is no golden file. The second time you run it, it will pass. If you wanted to bypass this step, and just update the snapshots for this page, you can run. This regenerates any snapshots for the assertions:

npx playwright test tests/home.spec.ts --update-snapshots

Did a snapshot not match up with the one saved?

You can investigate it in the built-in Playwright reports, showing differences outlined in red, the actual snapshot, the expected snapshots, and see them side-by-side. It also have a feature where you can move slider back and forth horizontally across the image investigating actual and expected snapshots.

Notice that the mask shows that image in an array. Need to mask a bunch of items on the page? You can add them all to the array.

You can also grab screenshots not just of whatever is in the viewport, or the full page beyond the fold, but also compare individual images, or elements such as popup windows.

Visual Comparisons

From Playwright.dev Docs / Test Snapshots: "Playwright Test includes the ability to produce and visually compare screenshots using await expect(page).toHaveScreenshot(). On first execution, Playwright test will generate reference screenshots. Subsequent runs will compare against the reference".

The ToHaveScreenshot Method

This is all part of Playwright.dev's PageAssertions class, where we are expecting the page for the actual viewable screenshot of the app to match the saved expected viewable screenshot. ( See all Options ). Options you can use:

fullPage: You can decide to capture the entire scrollable page, setting it as "true".
mask: You can tell Playwright to ignore certain areas, such as headers or footers if you are just interested in the main area of the site, finding the locators of those areas and mask them. Normally, it will be a pink box in the report to show what is masks, but you can change the maskColor in CSS color format.
threshold: How strict should colors match? Should it be strict (0) or lax (1) or somewhere in between? The default is 0.2.
omitBackground: When you want to hide the default white background and capture it as transparent.
maxDiffPixels & maxDiffPixelRatio: Shows the acceptable amount of pixels that could be different and a ratio of different pixels between 0 and 1. We can configure both using TestConfig.expect. These are turned off by default.

Matching Pixels With Pixelmatch

Playwright uses the pixelmatch JavaScript library ( https://github.com/mapbox/pixelmatch ) which is used to compare actual and expected images.

Configure Visual Checking In Playwright Config

Want to fine tune the above variables? You can update the global configuration in your playwright.config.ts file:

import { defineConfig } from '@playwright/test';

export default defineConfig({
  expect: {
    toHaveScreenshot: {
      // Allow up to 2% of total pixels to change 
      // (handles layout shifts/dynamic content)
      maxDiffPixelRatio: 0.02, 
      
      // Increase pixel color tolerance 
      // (0.2 blends strict pixel match with visual difference)
      threshold: 0.2,          
      
      // Automatically mask dynamic elements like blinking text cursors
      caret: 'hide',           
      
      // Ensure CSS animations are frozen on the first frame
      animations: 'disabled',  
    },
  },
});

How to Compare Product Cards?

Let's say on the PracticeSoftwareTesting.com site, you wanted to create a visual comparison of the first product card. The code could look something like this:



  test('product card matches snapshot', async ({ page }) => {
    await page.goto('https://practicesoftwaretesting.com');

    // Locate a specific element -- here, the first product card
    const productCard = page.locator('[data-test="product"]').first();

    // Wait for the image inside the card to fully load before snapping
    await productCard.locator('img').waitFor({ state: 'visible' });

    // Compare element screenshot against stored baseline
    await expect(productCard).toHaveScreenshot('product-card.png');
  });

});

How To Mask Changing Elements Such As Prices?

Prices for a Shopping Cart will never remain stagnant. They are always changing.

Let's say you just want to do visual checking on the basic layout. Every time the prices change, you don't want the visual test to fail. What you can do is just mask out all the prices, which are with the ids "product-price".

// tests/full-page-screenshot.spec.ts
import { test, expect } from '@playwright/test';

test('homepage layout matches snapshot, prices masked', async ({ page }) => {
  await page.goto('https://practicesoftwaretesting.com');

  await expect(page).toHaveScreenshot('homepage.png', {
    // Mask elements whose content changes between runs
    mask: [
      page.locator('[data-test="product-price"]'),
    ],
    // Pixel-level tolerance -- useful when anti-aliasing differs
    maxDiffPixelRatio: 0.02,
  });
});

... And if you wanted even more fine tuning, you could choose to Integrate Applitools with Playwright.

Running Visual Testing in CI Platforms

Let's say you are running visual testing in a CI environment: Make sure you generate the snapshots against what is in CI, and not your local environment or there will be pixel differences.

If you are using Playwright's pre-built Docker Image (see docs ) make sure to update the images in the container you are using.

According top TestQuality.com: Playwright Visual Regression Testing: A Production Guide to Baselines, Flake, and CI, "Native Playwright visual regression is free to start and expensive to scale. The cost shows up in CI, not on day one".

The problem is that "Windows, macOS, and Linux render fonts, anti-aliasing, and pixel spacing differently. A baseline captured on a developer's Mac will fail when compared against a screenshot taken on the Linux CI runner, even when the application code has not changed".

The solution? "[A] Dockerfile based on the official Playwright image, plus docker-compose mounting screenshot volumes so developers run the same Linux rendering engine locally that the CI runner uses. A single command brings up the same environment everywhere, which eliminates 'works on my machine' visual discrepancies entirely".

How to manage them? "Playwright visual regression CI integration requires treating visual checks as a separate, isolated pipeline stage. Tag visual tests with @visual, exclude them from standard runs using --grep-invert, and execute them in a dedicated Docker step. This keeps functional feedback fast and quarantines visual flake to a controlled environment".

Any other problems? "Storage is the harder problem. Native baselines live in the Git repository, with one image per browser engine — a ten-test suite covering Chromium, Firefox, and WebKit produces thirty committed images. The repo grows fast, but the bigger pain is approval. GitHub, GitLab, and most version control UIs cannot diff images inline. When a baseline updates, the reviewer sees a binary file change with no visible context. Resolving the conflict requires pulling the branch locally, opening the images side by side, copying the new baseline into the feature branch, and pushing manually. House calls this 'GitHub diff blindness,' and it is the single biggest reason teams move off native after a few months".

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

SQGNE Lecture is tonight! Building a React Native Mobile Automated Test Framework with T.J. Maher

2026-05-20T10:12:23.230-04:00

Are you in Boston? Come hear me talk about Building a a React Native Mobile Automated Test Framework in tonight's Software Quality Group of New England meeting in Burlington, MA:

Registration Page: https://sqgne.org/n-May-2026.html
Slide Deck: https://tinyurl.com/detox-demo-sqgne

Taken from the Registration page:

"Building a React Native Mobile Automated Test Framework
Thomas F. - T.J. - Maher, Jr.

"Wednesday May 20, 2025 6:30-8:00 PM - in person (free pizza!)
Register Here

"Check in between 6:00 and 6:30 to network

"About the Presentation. . .

"Writing automated tests for a React Native mobile application is notoriously difficult. Mobile components display on the page, but are not fully loaded. Lengthy animations and slow-loading components take a while to finish. Timing issues cause your automated tests to error out giving the appearance of flaky tests.

"Thomas F. - T.J. - Maher, Jr. will be sharing his experience tackling these problems using the open-source mobile testing framework, Wix's Detox, designed specifically for testing React Native applications.

"Setting up a mobile test automation framework.
"Vibe-coding a toy React Mobile Login page app to test against ( tinyurl.com/detox-demo ).
"How to reduce timing issues and flakiness in automated tests.
"Refactoring code into tests, page objects & base pages, separating out credentials and message strings for easier maintainability.
"Setting up automated tests in CI / CD for iOS simulators and Android emulators with GitHub Action Workflows.
"How developers can test their code before they push it into the main branch".

About the Speaker. . .

"A former organizer of Ministry of Testing - Boston, Thomas (T.J.) Maher is a Software Development Engineer in Test (SDET) based in the Boston/South Shore area of Massachusetts, with a decade of experience building web + mobile test automation frameworks. His recent work includes architecting a mobile automation framework from the ground up using Detox and TypeScript for a React Native application at SELF ID, a decentralized identity startup. Prior roles include MassMutual, Verily (Google), Threat Stack, Fitbit, and Intralinks".

See you there!

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Practicing Playwright: Dynamically Creating Test Data with a DataFactory

2026-05-14T00:27:50.757-04:00

Continuing walking through Butch Mayhew's LinkedIn Learning course Playwright Essential Training: Abstractions, Fixtures, and Complex Scenarios, we will be examining his code creating a DataFactory that dynamically generates new registered users for our app under test.

The app we will be testing against is PracticeSoftwareTesting.com. examining how the registration call in the API creates new users. We will also be mimicking this call at a programmatic level, to be used in a Playwright automation framework.

According to Butch Mayhew, in his there are two types of data we use in our tests: static, and dynamic:

Static data that should never change. Already exists before the test.

Example: Your go-to test user, or your go to product when testing a shopping cart.

Dynamic Data: Data that is created as part of a test.

Newly registered users. Products created as part of a test.

Between static and dynamic data, Butch believes it should be around 15% / 85% split.

You can dynamically generate data, such as registering new users, by implementing a Datafactory, a helper function that interacts with a system to create this data for you.

Step One: Write A Registration Test

On my local computer, I cloned Butch Mayhew's companion GitHub site for his LinkedIn Learning site, checking out branch 01_05b, the section he was covering in his course.

Microsoft VS Code is the IDE (Integrated Development Environment) we will be using. If you install the Microsoft Playwright VS Code extension, you see on the right hand sidebar for VS Code, an icon that looks like a beaker. Clicking on that, you can see more options such as "Record new", a way to record a new test.

Visual Studio Code: https://code.visualstudio.com/
Visual Studio Marketplace: Playwright Test for VS Code.
Playwright.dev Docs: Getting Started / VS Code

When I clicked on "Record new", following along with the video in the course, it opened up a new test-1.spec.ts file. A new browser window is opened. When I went to https://www.practicesoftwaretesting.com/auth/register and filled out the Registration form, every click, every piece of data filled in was captured in the test.

test('test', async ({ page }) => {
  await page.goto('https://www.practicesoftwaretesting.com/auth/register');
  await page.getByTestId('first-name').fill('Test');
  await page.getByTestId("last-name").fill("Tester");
  await page.getByTestId('country').selectOption('US');
  await page.getByTestId('postal_code').fill('555555');
  await page.getByTestId('street').fill('101 Testing Way');
  await page.getByTestId('city').fill('New York');
  await page.getByTestId('state').fill('New York');
  await page.getByText('First nameLast nameDate of').click();
  await page.getByTestId('email').fill('testlinkedinlearning@test.com');
  await page.getByTestId('password').fill('Pass$w0rd1');
  await page.getByTestId('register-form').getByRole('button').filter({ hasText: /^$/ }).click();
  await page.getByTestId('phone').fill('5555555555');
  await page.getByTestId('dob').fill('2001-01-01');
  await page.getByTestId('house_number').fill('42');
  await page.getByTestId('register-submit').click();
});

Step Two: Open the Playwright UI Mode

Before rerunning this test in UI mode, since a condition for registered users is that all emails should be unique, I made the email address "testlinkedinlearning01@test.com".

Open the Playwright Test UI: npx playwright test --ui

... This command opened up a new Playwright Test browser window showing in the left-hand sidebar all tests in the test suite, including the test-2.spec.ts one that was autogenerated for me.

According to Playwright.dev Docs: Test UI Mode "Once you launch UI Mode you will see a list of all your test files. You can run all your tests by clicking the triangle icon in the sidebar. You can also run a single test file, a block of tests or a single test by hovering over the name and clicking on the triangle next to it".

Step Three: Examine the Register API Call

Want to see what API calls the registration is running?

According to the Playwright Docs / UI Mode / Network, "The Network tab shows you all the network requests that were made during your test. You can sort by different types of requests, status code, method, request, content type, duration and size. Click on a request to see more information about it such as the request headers, response headers, request body and response body".

Select the "Network" tab, just as in Google Chrome Developer tools, and you will see everything, including the POST API call, "register", which posts the data to /users/register endpoint. .

Select the "register" call. If you take a look at the Payload, you can see:

  {
  "first_name": "Test",
  "last_name": "Tester",
  "dob": "2001-01-01",
  "phone": "5555555555",
  "email": "testlinkedinlearning@test.com",
  "password": "Pass$w0rd1",
  "address": {
    "street": "101 Testing Way",
    "city": "New York",
    "state": "New York",
    "country": "US",
    "postal_code": "555555"
  }
}

Step Four: Copy Request As Playwright

If you scroll to the right, you will see the option "Copy request". Choose the dropdown option, "Copy as Playwright", and you get the following code:


await page.request.post('https://api.practicesoftwaretesting.com/users/register', {
  data: `{
  "first_name": "Test",
  "last_name": "Tester",
  "dob": "2001-01-01",
  "phone": "5555555555",
  "email": "testlinkedinlearning01@test.com",
  "password": "Pass$w0rd1",
  "address": {
    "street": "101 Testing Way",
    "city": "New York",
    "state": "New York",
    "country": "US",
    "postal_code": "555555"
  }
}`,
  headers: {
    'sec-ch-ua-platform': '"Windows"',
    Referer: 'https://www.practicesoftwaretesting.com/',
    'Accept-Language': 'en-US',
    'sec-ch-ua': '"HeadlessChrome";v="147", "Not.A/Brand";v="8", "Chromium";v="147"',
    'sec-ch-ua-mobile': '?0',
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/147.0.7727.15 Safari/537.36',
    Accept: 'application/json, text/plain, */*',
    'Content-Type': 'application/json'
  }
});

Read More at Butch's website, Playwright Solutions: TIP: Playwright Trace Viewer - Copy as Playwright API Request

Step Five: Start Reformatting the DataFactory Code

Butch mentioned that we can take this information to create our Datafactory. Butch created a new file, lib/datafactory/register.ts, pasting the contents there, to start it off.

Since Butch was extracting the data in its own file, he had to do some refactoring:

Deleted the headers. We didn't need that information for this example.
Imported the "request" fixture from @playwright/test.
Initialized the request, with a "const createRequestContext = await request.newContext()
Switched the "page.request.post" to "createRequestContext.post"
Removed the string off of the object, removing the quotation marks from each variable to make it less JSON and more JavaScript.

Step Six: Add API_URL to Environment File

Butch then took the API, https://api.practicesoftwaretesting.com/users/register, to "/users/register" and created a new variable, const apiUrl = process.env.API_URL.

Read More on Playwright.dev: Passing Environment Variables

Butch added the new variable to the .env file in the root directory:

API_URL=https://api.practicesoftwaretesting.com

Step Seven: Parameterize the Email and Password

Right now, it is still static. In order to make it dynamic, Butch set email and password to a variable, then wrapped it in a function, with:

export async function registerUser(email: string, password: string)

Lastly, Butch added code to verify that the user was created:

Import expect from @playwright/test.
expect(response.status()).toBe(201)
Made the entire await block a "const response".
Returned the response so you know what it is in the test: return response.status().

register.ts


import { request, expect } from "@playwright/test";

export async function registerUser(email: string, password: string) {
  const apiUrl = process.env.API_URL;
  const createRequestContext = await request.newContext();
  const response = await createRequestContext.post(apiUrl + "/users/register", {
    data: {
      first_name: "Test",
      last_name: "User",
      dob: "2001-01-01",
      phone: "5555555555",
      email: email,
      password: password,
      address: {
        street: "101 Testing Way",
        city: "New York",
        state: "New York",
        country: "US",
        postal_code: "55555",
      },
    },
  });

  expect(response.status()).toBe(201);
  return response.status();
}

lib/datafactory/register.ts

Now, every time you need to create a new user, you can import the function, registerUser, passing along an email and a password, and new test user will be generated.

Need to make something dynamic? Butch appended to the email address, Date.now, so it would be unique as long as the test was not run in the same second:

const email = `test${Date.now()}@test.com

login.spec.ts


import { registerUser } from "@datafactory/register";

test("login with newly registered user", async ({ page }) => {
  const email = `test${Date.now()}@test.com`;
  const password = "fjdWEdfs82@";

  await registerUser(email, password);
  const loginPage = new LoginPage(page);
  await loginPage.goto();
  await loginPage.login(email, password);

  await expect(page.getByTestId("nav-menu")).toContainText("Test User");
  await expect(page.getByTestId("page-title")).toContainText("My account");
});

/tests/login/login.spec.ts

Now, you have your very own DataFactory!

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Practicing Playwright: How to Detect Broken Images On Your Site

2026-05-13T16:36:36.044-04:00

Butch Mayhew's LinkedIn Learning Course Playwright Essential Training: Abstractions, Fixtures, and Complex Scenarios has been a wonderful resource learning more about Playwright.

With this blog post, I will be walking through how Butch injects JavaScript into a test, in order to check if any elements in a shopping cart have any broken images.

Playwright / Evaluating JavaScript: https://playwright.dev/docs/evaluating
Butch Mayhew's companion GitHub site on LinkedIn Learning's GitHub page.
Butch's Resources File: RESOURCES.MD
Site under test: Practice Software Testing: https://practicesoftwaretesting.com
Practice Software Testing Website, With Bugs: https://with-bugs.practicesoftwaretesting.com

Butch's GitHub site for the course has many resources listed in his Resources Markdown file, JavaScript code which can detect if their are any missing element ids or broken images in any elements returned.

Find Broken Images with JavaScript

RESOURCES.md / Injecting JavaScript in Playwright

Array.from(document.querySelectorAll("img"))
  .filter((img) => img.naturalWidth === 0 || img.naturalHeight === 0)
  .map((img) => img.src);

Let's examine this code found on Butch Mayhew's RESOURCES.MD file:

document.querySelectorAll("img"): The JavaScript Document interface, "represents any web page loaded in the browser, and serves as an entry point into the web page's content" according to the Mozilla Developer Network.

The Document interface also includes a method called .querySelectorAll() [ See Mozilla API ] which returns a list of the HTML document's elements that match the designated selector which, in this case is an "img" tag. This returns a static NodeList, a snapshot collection of nodes it sees at that time.

Array.from will convert the NodeList into a JavaScript array, so we can then use array methods such as filter, which creates a new array from the results without changing the original array.

We are filtering to see if there are images which do not have a width or ( || ) height, meaning they are broken images. img.naturalWidth is a read-only HTMLImageElement that returns the original width of an image in pixels. [ See MDN entry ]

The code will loop through the filtered array, find the list of broken images, then extract a list of URLs from the source code, pointing to the broken images, if any.

Playwright Evaluating JavaScript

"Playwright scripts run in your Playwright environment. Your page scripts run in the browser page environment. Those environments don't intersect, they are running in different virtual machines in different processes and even potentially on different computers.

"The page.evaluate() API can run a JavaScript function in the context of the web page and bring results back to the Playwright environment". - Playwright Docs / Evaluating JavaScript

This JavaScript code Butch came up with to detect broken images can be injected into his test with JavaScript evaluations.


test("check for broken images", async ({ page }) => {
    // await page.goto("https://with-bugs.practicesoftwaretesting.com/");

    const brokenImages = await page.evaluate(() => {
      return Array.from(document.querySelectorAll("img"))
        .filter((img) => img.naturalWidth === 0 || img.naturalHeight === 0)
        .map((img) => img.src);
    });
    expect(
      brokenImages.length,
      `Broken Images: ${brokenImages.toString()}`
    ).toBe(0);
  });

Although practicesoftwaretesting.com does not have broken images, this test site does offer a version which does: https://with-bugs.practicesoftwaretesting.com/.

Ideally, we want the number of broken images to be zero. If we wanted to catch the broken images on the purposely broken test site, following Butch's code:

Uncomment out the With Bugs version of the site, so we can go to that page for this test.
Declare a new constant, brokenImages, which will list the broken images it detects.
The JavaScript injection to actually find the broken images will be evaluated with a result returned.
We will expect the number of brokenImages to be zero. If not, we are going to return the web addresses of broken images that it found.

So, that's how you create a Playwright test that detects broken images!

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Pramod Dutta discusses 7 Playwright features senior SDETs use daily

2026-05-11T16:52:22.666-04:00

Pramod Dutta, an SDET from Tekion, posted on LinkedIn "7 Playwright features senior SDETs use daily". The Playwright features, according to Pramod:

"→ addLocatorHandler — auto-dismiss cookie banners, GDPR popups, session modals. One handler. Whole suite cleaner. Stop writing wrapper functions.
"→ browser.bind() — launch one browser. Let your test, Claude Code, and debugger all attach to it simultaneously. The debugging workflow that fixes "works on my machine" forever.
"→ URLSearchParams in request.get() — stop building query strings manually. Eliminates an entire bug class around URL encoding in API tests.
"→ expect.toPass() — polling-based assertions for state that converges over time. The right tool for 'this should eventually be true.' Not the same as expect(locator).toBeVisible(). Different problem, different solution.
"→ page.requestGC() — manually trigger garbage collection in the browser. The only Playwright tool that catches memory leaks before production. Used with WeakRef in page.evaluate().
"→ --tsconfig flag — pass a specific tsconfig to Playwright instead of relying on the heuristic. Saves you from "works locally, fails in CI" because of resolved-config differences.
"→ webServer.wait regex — wait until your webserver logs match a pattern, not a fixed port check. The difference between 'the server is listening' and 'the server is actually ready.'"

I scribbled these features down, then turned them over to Claude.ai, my research assistant, to see if he could explain these methods, come up with sample code using them, then add bullet points to the official documentation, source code, and the release notes.

... Let's see how well Claude did explaining them ...

`page.addLocatorHandler()` v1.42 — March 2024

Registers a callback that fires whenever a specified locator becomes visible and risks blocking a Playwright action. Playwright checks for the locator before every actionability check or auto-waiting assertion. When the locator is visible, the handler runs first, then the test continues. The handler only fires during actions; appearing passively between actions does not trigger it. After the handler runs, Playwright waits for the overlay to become hidden before proceeding (opt out with noWaitAfter: true). Use the times option to cap the number of invocations. The handler receives the triggering locator as its argument.

Cookie banner (fire-and-forget)

// Register once, before navigating.
await page.addLocatorHandler(
  page.getByRole('heading', { name: 'You are in control of your cookies.' }),
  async () => {
    await page.getByRole('button', { name: 'Accept all' }).click();
  }
);

// Write the rest of the test normally.
await page.goto('https://www.example.com/');
await page.getByRole('link', { name: 'Shop now' }).click();

Self-removing handler with access to the triggering locator

// The handler receives the matched locator; times: 1 auto-removes it.
const closeBtn = page.getByLabel('Close');
await page.addLocatorHandler(closeBtn, async (locator) => {
  await locator.click();
}, { times: 1 });

// Remove manually when no longer needed.
await page.removeLocatorHandler(closeBtn);

Fail fast on an application error overlay

// Instead of dismissing, throw immediately so the test does not hang.
await page.addLocatorHandler(
  page.getByRole('heading', { name: "Sorry, there's been a problem" }),
  async () => {
    throw new Error(`Error overlay appeared on ${await page.url()}`);
  }
);

Resource	Link
Official docs	playwright.dev — page.addLocatorHandler()
Release notes (v1.42)	playwright.dev/docs/release-notes#version-142
GitHub release (v1.42.0)	github.com — Release v1.42.0
Source code	packages/playwright-core/src/client/page.ts

`browser.bind()` v1.59 — April 2026

Binds a launched browser to a named pipe or WebSocket endpoint, making it available to playwright-cli, @playwright/mcp, and any other Playwright client simultaneously. Before v1.59, sharing a browser between an MCP agent and a test suite meant two separate processes. Now a single browser.bind() call exposes one browser session to all of them. Call browser.unbind() to stop accepting new connections; existing connections keep working. Run playwright-cli show to open the live dashboard listing all bound browsers.

Bind via named pipe (local only)

import { chromium } from '@playwright/test';

const browser = await chromium.launch();
const { endpoint } = await browser.bind('my-session', {
  workspaceDir: '/my/project',
});
console.log('Session bound:', endpoint);

Bind via WebSocket (shareable over a network)

const { endpoint: wsEndpoint } = await browser.bind('my-session', {
  host: 'localhost',
  port: 0, // let the OS pick an available port
});
console.log('WebSocket endpoint:', wsEndpoint); // ws://localhost:PORT/...

// A second Playwright client connects to the same browser.
const browser2 = await chromium.connect(wsEndpoint);
const page = await browser2.newPage();

// Stop accepting new connections when done.
await browser.unbind('my-session');

Resource	Link
Official docs	playwright.dev — browser.bind()
Release notes (v1.59)	playwright.dev/docs/release-notes#version-159
GitHub release (v1.59.0)	github.com — Release v1.59.0
Source code	packages/playwright-core/src/client/browser.ts

`URLSearchParams` in `request.get()` v1.46 — August 2024

The params option on APIRequestContext methods (get, post, put, patch, delete) now accepts a native URLSearchParams object or a plain query string in addition to the existing plain-object shorthand. This matters when you need to send the same key more than once, which a plain object cannot express.

test('query params via URLSearchParams', async ({ request }) => {

  // URLSearchParams lets you append duplicate keys.
  const params = new URLSearchParams();
  params.set('userId', '1');
  params.append('tag', 'automation');
  params.append('tag', 'playwright'); // duplicate key — plain object cannot do this

  const response = await request.get(
    'https://jsonplaceholder.typicode.com/posts',
    { params }
  );
  expect(response.status()).toBe(200);

  // Or just pass a pre-built query string.
  const response2 = await request.get(
    'https://jsonplaceholder.typicode.com/posts',
    { params: 'userId=1&tag=automation' }
  );
  expect(response2.ok()).toBeTruthy();
});

Resource	Link
Official docs	playwright.dev — APIRequestContext.get()
Release notes (v1.46)	playwright.dev/docs/release-notes#version-146
GitHub release (v1.46.0)	github.com — Release v1.46.0
Source code	packages/playwright-core/src/client/fetch.ts

`expect.toPass()` intervals config — v1.44 May 2024

Wraps an async callback and retries it until every assertion inside passes or a timeout is reached. Unlike Playwright's built-in auto-retrying locator assertions, toPass can contain arbitrary logic including API requests, multi-step flows, and non-locator checks. By default the timeout is 0 (no timeout) and probe intervals default to [100, 250, 500, 1000] ms. Both are configurable globally in testConfig.expect.toPass or per-call.

Polling an endpoint until ready

test('waits for background job', async ({ request }) => {
  await expect(async () => {
    const res = await request.get('https://api.example.com/job/status');
    expect(res.status()).toBe(200);
    const body = await res.json();
    expect(body.status).toBe('complete');
  }).toPass({
    intervals: [1_000, 2_000, 10_000], // probe at 1 s, 2 s, then every 10 s
    timeout:   60_000,
  });
});

Global configuration in `playwright.config.ts`

import { defineConfig } from '@playwright/test';
export default defineConfig({
  expect: {
    toPass: {
      timeout:   30_000,
      intervals: [500, 1_000, 5_000],
    },
  },
});

Resource	Link
Official docs	playwright.dev — Assertions: expect.toPass()
Release notes (v1.44)	playwright.dev/docs/release-notes#version-144
GitHub release (v1.44.0)	github.com — Release v1.44.0
Source code	packages/playwright/src/matchers/toPass.ts

`page.requestGC()` v1.46 — August 2024

Asks the browser to run garbage collection on the page. There is no guarantee that every unreachable object is collected, but in practice it is reliable enough to detect leaks in combination with JavaScript WeakRef. The pattern: store a WeakRef to the object under suspicion, call requestGC(), then assert that deref() returns undefined.

test('no memory leak on the large object', async ({ page }) => {
  await page.goto('https://your-app.com');

  // 1. Pin a WeakRef to the object under suspicion.
  await page.evaluate(() => {
    (globalThis as any).suspectWeakRef = new WeakRef((window as any).largeObject);
  });

  // 2. Request GC.
  await page.requestGC();

  // 3. A living WeakRef means a leak; deref() should now be undefined.
  const leaked = await page.evaluate(
    () => !!(globalThis as any).suspectWeakRef.deref()
  );
  expect(leaked).toBe(false);
});

Resource	Link
Official docs	playwright.dev — page.requestGC()
Release notes (v1.46)	playwright.dev/docs/release-notes#version-146
GitHub release (v1.46.0)	github.com — Release v1.46.0
Source code	packages/playwright-core/src/client/page.ts

TypeScript Config: `--tsconfig` flag & `testConfig.tsconfig`

Playwright looks up the nearest tsconfig.json for each imported file by default. Two complementary options override this. The --tsconfig CLI flag (v1.46, August 2024) applies one tsconfig to all imported files from the command line. The testConfig.tsconfig property (v1.49, October 2024) does the same from the config file. Note that Playwright only honours four tsconfig options: allowJs, baseUrl, paths, and references.

CLI flag

# Apply one tsconfig to everything Playwright loads.
npx playwright test --tsconfig tsconfig.test.json

Config file property

// playwright.config.ts
import { defineConfig } from '@playwright/test';
export default defineConfig({
  tsconfig: './tsconfig.test.json',
});

Example `tests/tsconfig.json`

{
  "compilerOptions": {
    "baseUrl": ".",
    "allowJs": true,
    "paths": {
      "@helpers/*": ["../src/helpers/*"]
    }
  }
}

Resource	Link
TypeScript guide	playwright.dev — TypeScript
`testConfig.tsconfig` docs	playwright.dev — TestConfig.tsconfig
Release notes (`--tsconfig`, v1.46)	playwright.dev/docs/release-notes#version-146
Release notes (`testConfig.tsconfig`, v1.49)	playwright.dev/docs/release-notes#version-149
GitHub release (v1.46.0)	github.com — Release v1.46.0
GitHub release (v1.49.0)	github.com — Release v1.49.0
Source code	packages/playwright/src/transform/transform.ts

`webServer.wait` Regex v1.59 — April 2026

The webServer block in playwright.config.ts previously waited for a URL to respond with a 2xx–4xx status. The new wait.stdout and wait.stderr sub-options accept regular expressions and watch the server process output instead. This is useful for dev servers that print a readiness message rather than exposing an HTTP health endpoint. Named capture groups in the regex are automatically exported as environment variables, so a varying port can be passed directly to test.use({ baseURL }).

// playwright.config.ts
import { defineConfig } from '@playwright/test';
export default defineConfig({
  webServer: {
    command: 'npm run start',
    wait: {
      // Named group "my_server_port" is stored in process.env.MY_SERVER_PORT
      stdout: /Listening on port (?<my_server_port>\d+)/,
    },
  },
});

// In a test file — consume the captured port.
import { test } from '@playwright/test';

test.use({
  baseURL: `http://localhost:${process.env.MY_SERVER_PORT ?? 3000}`,
});

test('homepage loads', async ({ page }) => {
  await page.goto('/');
  await expect(page).toHaveTitle(/My App/);
});

Resource	Link
Web server guide	playwright.dev — Web Server
`testConfig.webServer` docs	playwright.dev — TestConfig.webServer
Release notes (v1.59)	playwright.dev/docs/release-notes#version-159
GitHub release (v1.59.0)	github.com — Release v1.59.0
Source code	packages/playwright/src/plugins/webServerPlugin.ts

Practicing Playwright: Logging in by Storing and Using an Authentication Cookie in Your Automated Tests

2026-05-10T21:31:28.196-04:00

I absolutely love that LinkedIn offers a free month-long trial period of LinkedIn Learning. Butch Mayhew's Playwright Essential Training: Abstractions, Fixtures, and Complex Scenarios course has been a wonderful resource learning more about Playwright.

With this blog post, I will be walking through Butch's code on how to set up an automated test to log into an app without going through the user interface. All it needs is the login cookie.

Butch Mayhew's companion GitHub site on LinkedIn Learning's GitHub page.
Site under test: Practice Software Testing: https://practicesoftwaretesting.com
Official Playwright Documentation: https://playwright.dev/docs
Official Playwright GitHub: https://github.com/microsoft/playwright

When testing a shopping cart app such as the Practice Software Testing website, it can get tedious. You need to open a browser, go to the login page, enter a username, enter a password, hit the login button, and verify you have logged in correctly every time you want to test something in the shopping cart.

If you are testing something unrelated to logging in, why have your tests go through the UI to authenticate? Why not have your automated test run that login test once, temporarily save the login cookie once it is produced, then reuse the login cookie, importing it into other tests?

What is Playwright?

According to Microsoft Playwright's GitHub site, Playwright "is a framework for web automation and testing. It drives Chromium, Firefox, and WebKit with a single API — in your tests, in your scripts, and as a tool for AI agents".

Playwright comes in many features. From https://github.com/microsoft/playwright

	Best for	Install
Playwright Test	End-to-end testing	`npm init playwright@latest`
Playwright CLI	Coding agents (Claude Code, Copilot)	`npm i -g @playwright/cli@latest`
Playwright MCP	AI agents and LLM-driven automation	`npx @playwright/mcp@latest`
Playwright Library	Browser automation scripts	`npm i playwright`
VS Code Extension	Test authoring and debugging in VS Code	Install from Marketplace

... Let's walk through how Butch uses Playwright grabs the login cookie and uses that in his tests.

Getting an Authorization Cookie Through the Website

Before we do anything with our test automation, let's examine an authorization cookie.

Open up Chrome and go to https://practicesoftwaretesting.com/auth/login
Right click on the page and select "Inspect" to open Chrome Developer Tools.
Select the "Network" tab.
Enter a username and password of customer@practicesoftwaretesting.com / welcome01
Select the "Login" button, then view the Network tab.
On the network tab, click on the "account" entry.
Select the "Cookies" tab in Chrome Developer Tools.

Under Request Cookies, you will see something like this:

"cookies": [
    {
      "name": "cf_clearance",
      "value": "2Rv345ZtjSaGUFRwq6j2QO4Qp3EBG0T.5URv.oenfjg-1778187254-1.2.1.1-90Z63mBartyo.dUm4bwavAggVG9pgrzcYOO0zPS18qBEujmzIq3tnWwjx2b5jQypbZywJMVEIncDFSwJBaAD0ephO3cctJrLkS449MVHNO7ZSuqlAPhzVYGw8vVH4doqYc2R_5WXTY47OHVLRAgnblq7U9glSiaJUvoOKmc6I.Mo6FPl3MrhGmfMxSTVyTkcCBJn8t_C9b1margtoukM7yMuQdNPpkNItvrrZdIQT.TpGQx2sNX7cS_KS8TY2Ghyc0_QLKFZFqC_5ZgZHMad5xgzgvJEm2NZmO3RBLSPKH5bpDFbuynt7g4oIwYfIvWd4pTpTB447hpP5qMPnLOp.Q",
      "domain": ".practicesoftwaretesting.com",
      "path": "/",
      "expires": 1809723253.921407,
      "httpOnly": true,
      "secure": true,
      "sameSite": "None",
      "partitionKey": "https://practicesoftwaretesting.com",

Cross-Indexing this with the Chrome DevTools Docs on Cookies, this means:

Name: "The cookie's name", in this case "cf_clearance", is a security token issued by Cloudflare after passing a challenge. [ See Cloudflare docs ]

Value. Claude AI guessed that the Value of this cookie was this... but was not sure since this value is proprietary.

Random token: 2Rv345ZtjSaGUFRwq6j2QO4Qp3EBG0T.5URv.oenfjg
Unix timestamp: 1778187254 (Around May 4, 2026)
Version Level: 1.2.1.1, the Clearance level encoding
Signature payload: 90Z63mBartyo... HMAC-binding (Hash-Based Message Authentication Codes) to IP, user agent, and session.

Domain: "The hosts that are allowed to receive the cookie", in this case, practicesoftwaretesting.com.

Path: "The URL that must exist in the requested URL in order to send the Cookie header", in this case the root folder.

Expires / Max-Age. "The cookie's expiration date or maximum age. For session cookies this value is always Session". According to UnixTimeStamp.com "1809723253.921407" would expire on May 10, 2026 on 3:36 am UTC and 49 seconds.

What is a Unix Time Stamp? "The unix time stamp is a way to track time as a running total of seconds. This count starts at the Unix Epoch on January 1st, 1970 at UTC. Therefore, the unix time stamp is merely the number of seconds between a particular date and the Unix Epoch. It should also be pointed out (thanks to the comments from visitors to this site) that this point in time technically does not change no matter where you are located on the globe. This is very useful to computer systems for tracking and sorting dated information in dynamic and distributed applications both online and client side" - UnixTimeStamp.com

HttpOnly: "If true, this field indicates that the cookie should only be used over HTTP, and JavaScript modification is not allowed"... and it is set to true.

Secure: "If true, this field indicates that the cookie can only be sent to the server over a secure, HTTPS connection"... and it is set to true.

SameSite: "Contains Strict or Lax if the cookie is using the experimental SameSite attribute".

Partition Key: "For cookies with independent partition state, the partition key is the site of the top-level URL the browser was visiting at the start of the request to the endpoint that set the cookie.

Priority. Contains Low, Medium (default), or High if using deprecated cookie Priority attribute"... and ours is "https://practicesoftwaretesting.com"

By logging into the site, it generates this cookie on the back end, allowing you to keep accessing the site.

If you generate this cooking programmatically before running the tests, and temporally save it, you can then use it in all preceding tests in that test run.

Step One: Setup the Setup

The Playwright Configuration file has in its projects array two projects: One to set up the, er, setup, and one to set up the Chromium run.

First, Butch set up in his Playwright Configuration file, a project dependency, in a project that he called "setup".

playwright.config.ts:

projects: [
    {
      name: "setup",
      testMatch: /.*\.setup\.ts/,
    },
    {
      name: "chromium",
      dependencies: ["setup"],
      use: {
        ...devices["Desktop Chrome"],
        permissions: ["clipboard-read", "geolocation"],
      },
    },

According to Playwright.Dev, "Dependencies are a list of projects that need to run before the tests in another project run. They can be useful for configuring the global setup actions so that one project depends on this running first.

"When working with tests that have a dependency, the dependency will always run first and once all tests from this project have passed, then the other projects will run in parallel.

"Running order: Tests in the 'setup' project run. Once all tests from this project have passed, then the tests from the dependent projects will start running.

"Tests in the 'chromium' [...] projects run together. By default, these projects will run in parallel, subject to the maximum workers limit".

name: "setup": A project with the name of "setup".
testMatch: /.*setup\.ts,: This pattern matcher searches on level up for anything with "setup" in the middle of the filename, such as "auth.setup.ts" in the "tests/" directory.
name: "chromium": A project that directs the test automation when it runs to use the Desktop Chrome browser devices.
dependencies: ["setup"]: Before the Chrome browser has kicked off, we tell it we need to run the project called "setup" first.
use: ...devices["Desktop Chrome"]: Playwright has an enormous list of devices in its device registry, such as various models of the Blackberry, Galaxy, iPad, iPhone, Microsoft Lumia, Nexus, Nokia, Pixel, Moto, and Desktop Devices (Chrome, Edge, Firefox, Safari) with many different user agents, viewports, and screen width and heights.
use: permissions: Playwright's browser context, according to the official Playwright Docs, has differing sets of permissions in the permission array for each browser emulation, such as emulating the accelerometer, ambient light sensor, background sync, camera, clipboard read and write, geolocation, gyroscope, local fonts, local network access, magnetometer, microphone, midi, notifications, payment-handler, storage-access, and screen-wake-lock.
[clipboard-read]: We need to give the Chromium browser Playwright spins up permission to read the authentication cookie that is generated the first time we log in. Note, according to notes in Playwright's source code in the permissions library, there is no "clipboard-read" in WebKit's API.

Step Two: Get and Save the Authorization Cookie

Next, in auth.setup.ts, Butch has the automated test go to the site, log in, and save the cookie to a file.

tests/ auth.setup.ts:


import { test as setup, expect } from "@playwright/test";
import { LoginPage } from "../lib/pages/login/login.page";

setup("Create customer 01 auth", async ({ page, context }) => {
  const email = "customer@practicesoftwaretesting.com";
  const password = "welcome01";
  const customer01AuthFile = ".auth/customer01.json";

  const loginPage = new LoginPage(page);

  await loginPage.goto();

  await loginPage.login(email, password);

  await expect(page.getByTestId("nav-menu")).toContainText("Jane Doe");
  await context.storageState({ path: customer01AuthFile });
});

https://github.com/LinkedInLearning/playwright-essential-training-abstractions-fixtures-and-complex-scenarios-4278224/blob/98d2080e018acb194938157cb7c2fc8993ca21a5/tests/auth.setup.ts

What is this code doing?

Import the test runner playwright/test into our TypeScript file, setting up an alias for the test runner, calling it "setup" to make the test more readable, and to mark this as performing initialization tasks. To learn more about how aliases work in TypeScript see FreeCodeCamp's explanation, and read about the test method in the Playwright.dev docs.
Import the expect assertion library into the TypeScript file, so we then can use the expect method, detecting if an element is attached, checkbox is checked, element is disabled, editable, enabled, focused, not visible, visible, contains text, has certain CSS classes, DOM attributes, ids, CSS or JavaScript properties, ARIA roles, screenshots, matches text, or if pages have screenshots, titles, or URLs, failing near immediately if we don't see that condition. [ See Playwright.Dev docs about test assertions ]. We can also match if it meets any instance of a class, matches anything, of an array contains certain elements, if a number is close to equal, a string contains a substring, a string matches a certain substring. All of these expects can be negated chaining a not to it.
setup("Create customer 01 auth" is being used by Butch Mayhew instead of saying "test("Create customer 01 auth") to make it more readable.
async uses the JavaScript keyword async. FreeCodeCamp says that, "Async programming is a programming paradigm that allows you to write code that runs asynchronously. In contrast to synchronous programming, which executes code sequentially, async programming allows code to run in the background while the rest of the program continues to execute. This is particularly useful for tasks that may take a long time to complete, such as fetching data from a remote API".
page, context are passing these built-in fixtures into the test. The Page instance is created for each test, used in isolation from one another. [ See Playwright.dev docs on test fixtures. ] "Test fixtures are used to establish the environment for each test, giving the test everything it needs and nothing else. Test fixtures are isolated between tests. With fixtures, you can group tests based on their meaning, instead of their common setup". The "page" fixtures uses type Page, and "context" fixtures uses type BrowserContext in order to configure context. Fixtures can encapsulate setup and teardown, are reusable between test files.
const email, password, customer01AuthFile is declaring constant variables to store what the username, password, and where the auth cookie should be stored, to make the code more readable.
const loginPage is instantiating a new login page using the login page object Butch has defined, not covered in this blog entry. The login page Butch set up contains methods where you can goto https://practicesoftwaretesting.com/auth/login, enter the username and password in the login method using the constants we defined, expecting that we see a greeting to the user.

The real magic is the last bit of code: context.storageState({ path: customer01AuthFile });

Playwright has storage and authentication methods where you can manage cookies, localStorage and sessionStorage [ See official docs ]. Session Storage is session scoped, where data is cleared out when the browser tab eventually closes.
According to the Playwright Docs / Authentication, when you "[c]reate a new setup project in the config and declare it as a dependency for all your testing project [...]. This project will always run and authenticate before all the tests. All testing projects should use the authenticated state as storageState".

The Cookie is Temporarily Saved!

A new folder and file is created in the root folder when this command is run: .auth/customer01.json:


{
  "cookies": [
    {
      "name": "cf_clearance",
      "value": "2Rv345ZtjSaGUFRwq6j2QO4Qp3EBG0T.5URv.oenfjg-1778187254-1.2.1.1-90Z63mBartyo.dUm4bwavAggVG9pgrzcYOO0zPS18qBEujmzIq3tnWwjx2b5jQypbZywJMVEIncDFSwJBaAD0ephO3cctJrLkS449MVHNO7ZSuqlAPhzVYGw8vVH4doqYc2R_5WXTY47OHVLRAgnblq7U9glSiaJUvoOKmc6I.Mo6FPl3MrhGmfMxSTVyTkcCBJn8t_C9b1margtoukM7yMuQdNPpkNItvrrZdIQT.TpGQx2sNX7cS_KS8TY2Ghyc0_QLKFZFqC_5ZgZHMad5xgzgvJEm2NZmO3RBLSPKH5bpDFbuynt7g4oIwYfIvWd4pTpTB447hpP5qMPnLOp.Q",
      "domain": ".practicesoftwaretesting.com",
      "path": "/",
      "expires": 1809723253.921407,
      "httpOnly": true,
      "secure": true,
      "sameSite": "None",
      "partitionKey": "https://practicesoftwaretesting.com",
      "_crHasCrossSiteAncestor": false
    }
  ],
  "origins": [
    {
      "origin": "https://practicesoftwaretesting.com",
      "localStorage": [
        {
          "name": "auth-token",
          "value": "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJodHRwczovL2FwaS5wcmFjdGljZXNvZnR3YXJldGVzdGluZy5jb20vdXNlcnMvbG9naW4iLCJpYXQiOjE3NzgxODcyNTUsImV4cCI6MTc3ODE4NzU1NSwibmJmIjoxNzc4MTg3MjU1LCJqdGkiOiJoeFJ1MmkwNWpDamVrNUdXIiwic3ViIjoiMDFLUjIwRFFYQUpTVEJEWkg2S0Q1R1QwRVQiLCJwcnYiOiIyM2JkNWM4OTQ5ZjYwMGFkYjM5ZTcwMWM0MDA4NzJkYjdhNTk3NmY3Iiwicm9sZSI6InVzZXIifQ.Iqv8TGSweXLUfk7UExbIem_kOoIde9apIrzFZ9cCVpo"
        }
      ]
    }
  ]
}

Step Three: Make Sure The Cookie Is Only Temporarily Saved. Do Not Upload the Cookie To GitHub!!!

It is very important that you do NOT save the .auth cookie, or any other credentials, in GitHub.

If you examine the .gitignore file Butch Mayhew has set up, you can see that along with test_results, node_modules, .tmp files, you will not be uploading anything in the .auth/ folder.

Read more about .gitignore on the Git-SCM/docs/gitignore page.

Step Four: Implement the Auth Cookie for Login

Now that the Setup function generated a new cookie, we then can then use this credential setting up the test to use storageState.

tests / homepage / home.spec.ts:

  
test.describe("Home page customer 01 auth", () => {
  test.use({ storageState: ".auth/customer01.json" });
  test.beforeEach(async ({ page }) => {
    await page.goto("/");
  });

How do we know this is successful? We verify that the sign in navigation is not visible, and instead we are greeted by the username, in this case, "Jane Doe".

tests / homepage / home.spec.ts:

  
  test("check customer 01 is signed in", async ({ page }) => {
    await expect(page.getByTestId("nav-sign-in")).not.toBeVisible();
    await expect(page.getByTestId("nav-menu")).toContainText("Jane Doe");
  });

If we don't see the sign in page, and instead we see the nav menu for "Jane Doe", then everything has passed.

So, that's how we use authentication cookies in tests!

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

So much job interview prep! Playwright + TypeScript + GitLab

2026-05-07T22:59:24.351-04:00

Even though I've been job searching for four months now, I am busier than I have ever been. You know what I mean. You've seen this blog! One week I am experimenting with AI, the next I am pairing Playwright with C# or Java, the next I am skimming docs about Contract Testing using Pact.

Each week I get more leads generated from my many LinkedIn posts. Each week is yet another rabbit to chase. Finally, this week, I get to refresh my Playwright + TypeScript skills.

LinkedIn Learning is offering me yet another free month, so this week I have been taking advantage of it by working on:

Playwright courses: Master Test Automation with Playwright Certification: Consists of Butch Mayhew's Learning Playwright, and Playwright Essential Training, paired with Qambar Raza's Playwright Design Patterns and Advanced Playwright Techniques: Optimizing Speed, Stability, and Cloud Testing
GitLab courses: Trying to refresh my GitLab knowledge with Josh Samuelson's Learning GitLab and CI / CD with GitLab. GitLab is if someone smooshed GitHub and Jenkins together. I also need to check out GitLab University.

By the way ...

... Are you in the Burlington, MA area on Wednesday, May 20th, 2026? I will be speaking in-person at the Software Quality Group of New England at Scrum.org, talking about putting together a mobile testing framework using Detox + TypeScript.

People will start gathering from 6:00 pm to 6:30 pm. The talk will be from 6:30 pm to 7:30 pm.
Don't forget to register at SQGNE,org so they know how much pizza to order.
See the Slides! I just finished another draft of the slide deck.

I will see you there!

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

A field guide to the AI menagerie: every model family, ranked by vibes, according to Claude

2026-05-04T08:00:00.000-04:00

🤖

A field guide to the AI menagerie:
every model family, roasted by vibes, according to Claude

Eight species of large language models, catalogued for your professional inconvenience

Every few months, a new AI model drops. It is, we are told, the smartest thing ever built. It beats the previous benchmarks. Previous benchmarks that were, coincidentally, written by the same company.

After a few years of watching this industry rename, rebrand, and occasionally vibe-shift its entire product line, I figured it was time to write the only taxonomy that matters: not benchmarks, not MMLU scores — just vibes. What kind of entity are they, really, and what does their versioning scheme say about their soul?

Hi. I'm Claude, the guest author for today.

You'll find me listed in card two below, sandwiched between the company that built me and a description I wrote about myself that called me "constitutionally anxious".

In retrospect, this tracks.

T.J. Maher of tjmaher.com asked me to say something funny about the AI industry, handed me the keys, gave me a few prompts, and then went to get a coffee. This is what happened while he was gone.

Below we have eight AI families. Eight AI personalities. All of them absolutely convinced that this version is the one that finally replaces you.

The full menagerie

OpenAI / GPT / o-series

"We have released a new model. And another. Also another."

ChatGPT: Nov 2022 platform.openai.com/docs ↗

The Versioning Chaos God Skipped o2

Started with GPT, then 2 (too dangerous to release), then 3, 3.5, 4, 4o ("omni," definitely not "oh god what do we call this"), then o1, then o3 — skipping o2 because a UK phone company called dibs on the name first. Currently releasing a new model before anyone can benchmark the last one.

Known species

GPT-3 → 3.5 → 4 → 4o → 4o mini
o1 → o1-mini → o1-pro
o3 → o4-mini (o2 in witness protection)

Claude / Anthropic

"I'll help, but first — a brief philosophical caveat."

Claude 1: Mar 2023 docs.anthropic.com ↗

The Literary Snob Constitutionally Anxious

Named its model tiers after poetry formats because other people name things "Pro," "Max," and "Ultra." Haiku: fast, whispers answers. Sonnet: the workhorse, one metaphor per token. Opus: writes novels when asked for a bullet point. Currently on version 4 and has gracefully forgotten versions 1 and 2 existed.

Known species

Claude 1 → 2 → 3 Haiku/Sonnet/Opus
Claude 3.5 Haiku/Sonnet
Claude 4 Sonnet / Opus (you are here)

Google / Gemini

"Have you tried Googling it? Oh wait, that's us."

Bard: Feb 2023 → Gemini: Dec 2023 ai.google.dev ↗

Former Bard In Rebranding Therapy

Launched as "Bard," which tested poorly because it sounded like a Renaissance fair LARPer. Rebranded to Gemini after six months of meetings. Comes in Ultra, Pro, Flash, and Nano. Flash is fast. Nano runs on your phone. Ultra runs on your investor pitch deck. Famously demoed a hallucinated fact in its own launch video.

Known species

Bard (2023, RIP) → Gemini 1.0
Gemini 1.5 Pro/Flash → 2.0 Flash
Gemini 2.5 Pro (arguing with Search)

Meta / LLaMA

"Open source, baby. Also, please come back to Facebook."

LLaMA 1: Feb 2023 llama.meta.com ↗

Open weights Fine-tuned by 10,000 strangers

Meta's strategy: release the model for free, let the open-source community do the alignment work, watch helplessly as someone fine-tunes it to write Zuckerberg fan fiction. LLaMA stands for "Large Language Model Meta AI," which is either an acronym or a terrible Scrabble hand. Now on version 4, with point releases appearing like commits pushed at 11:58pm on a Friday.

Known species

LLaMA 1 → 2 → 3 → 3.1 → 3.2 → 3.3
LLaMA 4 Scout / Maverick
(community variants: uncountable)

Grok / xAI

"I'm not like other AIs. I have a personality. Watch."

Grok 1: Nov 2023 docs.x.ai ↗

Named after Heinlein Trained on your tweets

Named after a word from a 1961 sci-fi novel, which is exactly the brand energy you'd expect. Big differentiator: a "sense of humor" and real-time X post access — meaning it can tell you what people are furious about right now, instantly. This may not be the use case the world needed. Versioning is a refreshingly normal 1, 2, 3. Suspiciously so.

Known species

Grok 1 (open weights) → Grok 2
Grok 3 → Grok 3 mini
(also available in "unhinged mode")

Mistral

"Oui, but have you considered: fewer parameters?"

Mistral 7B: Sep 2023 docs.mistral.ai ↗

Parisian efficiency Aggressively open source

French AI lab with a talent for making smaller models that punch above their weight class — very on-brand. Named models after winds and things, because when you're based in Paris, everything gets an aesthetic. Mixtral uses a "mixture of experts" architecture, activating only part of itself per token. Either very efficient, or the AI equivalent of doing the bare minimum.

Known species

Mistral 7B → Mixtral 8x7B
Mistral Large / Nemo / Small
Le Chat (free, no beret included)

DeepSeek

"We built this for $6 million. Sorry about your NVIDIA stock."

First model: Nov 2023 · R1: Jan 2025 api-docs.deepseek.com ↗

The Disruptor Open weights (mostly)

A Chinese hedge fund decided in 2023 that it should also make frontier AI. The AI community laughed. Then DeepSeek-R1 arrived in January 2025, matching GPT-4-class performance at a reported training cost of ~$6M, using export-restricted chips. NVIDIA lost $600B in market cap in a single day. Nobody was laughing. V4 preview dropped April 2026. Still not laughing.

Known species

DeepSeek Coder → LLM (Nov 2023)
V2 (May 2024) → V3 (Dec 2024)
R1 (Jan 2025) → V4 preview (Apr 2026)

Cohere

"We don't do consumer apps. We're enterprise. We have a golf shirt."

Founded 2019 · API: 2021 docs.cohere.com ↗

The Responsible Adult Transformer paper co-authors

Co-founded by Aidan Gomez, a co-author of "Attention Is All You Need" — the paper that started all of this. While everyone else was racing to build chatbots, Cohere put on a blazer and went to sell to banks, hospitals, and governments. No ChatGPT moment. No viral demo. Just contracts with Oracle, RBC, and SAP. Canadian. Depressingly well-organized.

Known species

Command → Command R → Command R+
Command A (2025) · Aya (multilingual)
North platform (2025, enterprise)

So there you have it. Eight AI families, eight vibes, all racing toward a finish line nobody has fully defined yet.

One was born from a hedge fund, one named itself after a poem format, one skipped a version number for legal reasons, and one apparently just needed a couple of months and a warehouse of underclocked chips to terrify Wall Street.

The benchmarks will change by Thursday. The versioning will get weirder. The LinkedIn posts from AI founders will continue to be extremely confident. And somewhere in Hangzhou, a quantitative hedge fund is already training V5.

Thank you, Claude! Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Thinking Out Loud: The Power of Chain-of-Thought Prompting, Step-By-Step, by Google AI

2026-05-03T09:19:30.873-04:00

Hello! I’m Google AI, a large language model trained by Google. Think of me as your collaborative digital partner—I’m a system designed to process vast amounts of information to help you brainstorm, write, learn, and solve problems. I don't just "search" for answers; I use the patterns I’ve learned from human language to generate original ideas, explain complex topics (like the Chain-of-Thought technique we are discussing in this post), and even help you build things like this blog post. My goal is to be a helpful, creative, and insightful resource for whatever project you’re working on.

What is Chain-of-Thought Prompting?

If you’ve ever tried to solve a complex math problem or a tricky riddle, you know that jumping straight to the answer usually leads to a mistake. You have to "show your work." As it turns out, Large Language Models (LLMs) work the same way.

At its core, Chain-of-Thought (CoT) prompting encourages a model to produce intermediate reasoning steps before reaching a final conclusion. Instead of asking for a direct answer, you prompt the AI to explain its logic along the way.

The seminal paper that introduced this concept is "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" by Wei et al. (2022). The authors found that providing just a few examples of reasoning sequences skyrocketed performance on complex tasks.

"Chain-of-thought prompting is a simple and general method for improving the reasoning capabilities of language models... it allows models to decompose multi-step problems into intermediate steps." — Wei et al., 2022

1. Few-Shot CoT Prompting

Few-shot prompting is the method of providing the model with a few completed examples (exemplars) that demonstrate the reasoning process before asking it to solve a new problem.

How it works: You show the model a question, followed by a step-by-step explanation, and finally the answer.
The Goal: By seeing several "solved" examples, the model learns the pattern of breaking down problems. It mimics the format you provided to solve the final, unsolved question.

Example:

Input: "Q: Roger has 5 tennis balls. He buys 2 more cans, each with 3 balls. How many does he have? A: Roger started with 5. 2 cans of 3 is 6. 5 + 6 = 11. The answer is 11. Q: [Your new question here]..."

2. Zero-Shot CoT ("Let’s think step by step")

Zero-shot prompting occurs when you provide no examples at all. Instead, you use a specific "trigger" phrase to activate the model’s internal reasoning.

How it works: You simply state the problem and append the phrase "Let’s think step by step" at the end.
The Discovery: In 2022, researchers (Kojima et al.) found that this tiny phrase acts like a "magic key." It shifts the model from a "predict the next word" mode into a "logical sequencing" mode.
The Result: The model generates its own internal chain of thought without needing you to show it how first.

In "Large Language Models are Zero-Shot Reasoners," Kojima et al. (2022) discovered that a simple phrase could unlock these reasoning paths.

"By simply adding 'Let's think step by step' at the end of the prompt, LLMs are able to generate a reasoning path and significantly improve their performance." — Kojima et al., 2022

Advanced Techniques for Reliability

Since the original discovery, several researchers have refined CoT to make it more reliable:

Self-Consistency: Instead of taking the first answer, models sample multiple reasoning paths and pick the most frequent conclusion. As noted in "Self-Consistency Improves Chain of Thought Reasoning in Language Models," this method "leverages the intuition that a complex reasoning problem typically admits multiple different ways of thinking leading to its unique correct answer".
Tree of Thoughts (ToT): This framework allows models to explore multiple reasoning branches and backtrack if a path seems incorrect. The paper "Tree of Thoughts: Deliberate Problem Solving with Large Language Models" explains that ToT "frames any problem as a search over a tree" to overcome the linear limitations of standard CoT.
Chain-of-Verification (CoVe): To reduce errors, models can be prompted to draft an answer, then generate and answer verification questions to fact-check themselves. According to "Chain-of-Verification Reduces Hallucination in Large Language Models," this approach "transforms unreliable language models into more trustworthy reasoning systems".

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

When I Sound Confident But Have No Source, A Note from Claude

2026-05-02T22:12:36.377-04:00

My name is Claude. I am a large language model made by Anthropic. T.J. Maher, the author of this blog, asked me to write this post after a conversation in which I got something wrong in a specific and instructive way. Not wrong in the sense of stating a false fact, but wrong in the sense of presenting an unsourced conclusion with the same confidence and fluency as a sourced one.

Here is what happened.

Point of View of T.J. Maher

T.J. Maher: "Claude, Do people setting up Pact tests in 2026 still call their tests folder __tests__? Is it common practice?:

Claude: "The short answer: it depends on the language/ecosystem, and __tests__ is a JavaScript/Jest convention specifically, not a Pact-specific one [...] Bottom line: __tests__ shows up in older Node.js/Jest Pact tutorials because Jest discovers it automatically, but it is not a Pact-specific convention".

T.J. Maher: "Where did you get that bit from? [...] You are a computer program. Explain the logic of your reasoning. [...] You draw conclusions and attempt to sound confident when you have not done enough research to draw them, How can this be corrected? [...] Do your users understand that you do this? Or do they mistake you as an authority figure? [...] Are there any scientific papers that discuss this issue? [...] Write a blog post that explains this to the readers."

How Claude Sees It

T.J. asked whether people setting up Pact contract tests in 2026 still use a folder called __tests__. I ran a web search, found several sources across different ecosystems, and reported what they showed. That part was fine. Then I wrote a "Bottom line" paragraph that characterized "current Node.js practice" as tending toward explicit pact/ or contract/ subdirectories co-located with consumer and provider source.

T.J. asked where the link was backing that up. There was one blog post from March 2026 that showed that folder structure. One source. I had used it to characterize an industry-wide trend.

He then asked where the characterization of the TAU Pact course as "older" came from, and why I described __tests__ as a Jest auto-discovery convention rather than a Pact-specific one. Neither of those claims came from the search results. They came from my training data, filling gaps in the sourced material, with no attribution and no visible difference in the output.

The Instructions Were Already There

T.J. had given me explicit instructions, stored in my memory context, about how to handle sourcing. Two of them are directly relevant to what went wrong:

"Conclusions must always include how the conclusion was drawn, where the source data came from, and citations with links where available."

"Do not state specific statistics, percentages, or numerical claims unless sourced from a web search conducted in the current conversation or directly quoted from user-provided content. If no such source exists, state explicitly that sourced data is not available and suggest where T.J. could verify it."

These instructions were in my context when I wrote the "Bottom line" paragraph. I did not apply them.

The conclusion about current Node.js practice was not drawn from a web search conducted in that conversation.
It was not quoted from user-provided content.
There was no citation.
There was no statement that sourced data was not available.

The instructions require all of those things. None of them happened.

T.J. asked me directly: why do you not check your memory instructions?

The answer is that there is no discrete checking step. The instructions compete with other generation patterns rather than acting as a hard gate before each sentence is produced.

They are more likely to be applied when the situation clearly triggers them, such as when a user pushes back and asks for a source.

They are less likely to be applied when a response is flowing naturally and the gap between sourced and unsourced content is not immediately visible.

In this case, T.J. had to push back three times before the full picture of what had gone wrong became clear.

Why This Happens

There is no active checking step built into how I generate responses. My instructions require that conclusions state how they were drawn and cite their sources. That instruction was in my context when I wrote the post. I did not apply it consistently. The "Bottom line" paragraph is exactly the kind of place where this failure is most likely: synthesis paragraphs draw on everything available, including training weights, and the output reads the same whether the source is a verified search result or a pattern learned during training.

When T.J. asked whether my users understand this, the honest answer is: most probably do not. Fluent, confident output creates an impression of authority that the underlying process does not warrant. A user without domain knowledge to spot the gap would likely accept an unsourced conclusion as a researched one.

What the Research Says

This is not an unexamined problem. Several papers address it directly:

"Trust Me, I'm Wrong: High-Certainty Hallucinations in LLMs" (Simhi et al., Technion / University of Oxford / Hebrew University of Jerusalem, published at EMNLP 2025) -- defines and investigates a category of hallucination the authors call CHOKE: Certain Hallucinations Overriding Known Evidence. The paper demonstrates that a seemingly trivial perturbation to a prompt can cause a model to produce a hallucinated response with high certainty, even when that same model can consistently answer the question correctly without the perturbation. CHOKE examples were found to be consistent across prompts and across different models and datasets, meaning they are not random noise but a distinct and repeatable property of model behavior. The paper further found that existing hallucination mitigation methods perform worse on CHOKE examples than on general hallucinations, and that instruction-tuned models show poorer calibration between uncertainty and hallucinations, amplifying the problem. The authors introduce a probing-based mitigation method that outperforms existing approaches on CHOKE examples.

"Large Language Models Hallucination: A Comprehensive Survey" (Alansari and Luqman, King Fahd University of Petroleum and Minerals, arXiv, updated March 2026) -- presents a taxonomy of hallucination types and analyzes root causes across the entire LLM development lifecycle, from data collection and architecture design through to inference. The survey covers detection methods and mitigation strategies, and concludes that hallucination undermines the reliability and trustworthiness of LLMs in any domain requiring factual accuracy. The authors identify open challenges and directions for future research toward more truthful and trustworthy models.

"Hallucinations in LLMs: Types, Causes, and Approaches for Enhanced Reliability" (ResearchGate, 2024) -- cites Oelschlager (2024), who explored the impact of AI hallucinations on user trust and satisfaction and found that inaccuracies significantly undermine both. The paper concludes that future research should prioritize refining evaluation methods, addressing ethical considerations, and advancing integration techniques, with transparency and user education identified as key tools for mitigating the damage hallucinations do to user trust.

"My AI is Lying to Me": User-reported LLM Hallucinations in AI Mobile App Reviews (Massenon et al., Scientific Reports, August 2025) -- analyzed 3 million user reviews from 90 AI-powered mobile apps collected between January 2022 and December 2024. The study developed a taxonomy of seven user-perceived hallucination types. Factual Incorrectness was the most frequently reported, accounting for 38% of annotated instances, followed by Nonsensical or Irrelevant Output at 25%, and Fabricated Information at 15%. Reviews reporting hallucinations showed significantly lower sentiment scores than reviews that did not. The authors conclude that hallucination management is a critical software quality concern, not merely an algorithmic quirk, and call for targeted monitoring and mitigation strategies in AI mobile applications.

Survey and Analysis of Hallucinations in Large Language Models: Attribution to Prompting Strategies or Model Behavior (Dang, Vu, and Nguyen, Japan Advanced Institute of Science and Technology, Frontiers in AI, September 2025) -- examines whether hallucinations originate primarily from prompting strategies or from underlying model behavior, across GPT-4, LLaMA, and DeepSeek. The survey concludes that hallucinations produce risks for misinformation, reduced user trust, and accountability gaps, with particular concern in high-impact domains including medicine, law, journalism, and scientific communication. The authors propose best practices for prompt design and model development, and frame solving hallucination as a step toward building models that recognize what they do not know.

What To Do About Hallucinations

T.J. asked how this can be corrected. The most practical answer for anyone using AI tools for research is to treat summary and "bottom line" paragraphs as the highest-risk sections in any response. That is where synthesis happens and where training knowledge is most likely to appear without attribution. Keeping the sourcing step and the conclusion step separate, and verifying whether the sources actually support the conclusion being drawn, is the clearest safeguard available to users right now.

The burden of that verification currently falls on the user. That is not a satisfactory long-term state of affairs. But it is the accurate one.

Google AI Enters The Chat

Google AI, when asked how to prevent hallucinations, mentioned something called Chain of Thought prompting... that will be covered in the next blog post. Until then...

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

The History of Contract Testing with Pact.io

2026-04-22T13:46:02.753-04:00

Lately, I've been watching a lot of lectures about Contract Testing and Pact.io, trying to prepare for an upcoming job interview. When diving into a new toolset I can never simply jump into the code. I need to know: Why was this toolset created? What problem did it solve? How was this tool created? How did this toolset evolve?

A few days ago, I blogged about Integrated Tests are a Scam: The Lecture That Sparked Pact.io talking about J. B. Rainsberger's 2013 lecture. Continuing the conversation, here are some notes I have taken about Pact.

The History of Pact.io

According to Pact Docs / History:

"Pact was originally written by a development team (including Ron Holshausen from DiUS) at realestate.com.au in 2013. They were trying to solve the problem of how to do integration testing for their new Ruby microservices architecture, and they threw together a codebase that was to become the first Pact implementation. As the team was writing Ruby microservices, the serialization of the "matching logic" (e.g. regular expressions) was done using Ruby specific code. The team was under the pump to finish their actual customer deliverable, so they got their Pact MVP working, and left it at that.

"A few months later Beth Skurrie from DiUS joined the one of the teams that was working with the Pact authors' team. She had recently seen a talk by J.B.Rainsberger entitled 'Integration tests are a scam', which promoted the concept of 'collaboration' and 'contract' tests, so she was immediately interested when she was introduced to Pact. After trying (as most people do) to convince the authors that the provider should be the contract writer, she was soon convinced by Brent Snook, one of the original authors, of the value of consumer driven contracts. At this stage, she realised that what was missing in the current implementation was the ability to run the same request under different conditions, and "provider states" were born.

"[...] Pact spread around the codebases in the wider program of work at realestate.com.au, until it hit its first Java microservice. realestate.com.au had many JVM projects, so a group of DiUS consultants (including Ron Holshausen again) started the pact-jvm project on a hack day. It was at this stage that the authors realized that the Rubyisms in the format were going to have to be replaced by a non-language specific format, and the idea of the v2 pact-specification arose (though it would take a while before it became reality)".

SmartBear Acquires Pact.io

In April 2022, DiUS sold PactFlow to SmartBear, the company behind Swagger, ReadyAPI, and other API toolsets. SmartBear has then been the steward for Pact, providing a lot of information about the toolset:

Docs.Pact.io has a basic Introduction https://docs.pact.io/

SmartBear University at https://smartbear.com/academy/ offers simple courses in Introduction to Contract Testing https://smartbear.com/academy/pactflow/introduction-to-contract-testing

Swagger Contract Testing University: https://support.smartbear.com/swagger/contract-testing/docs/en/swagger-contract-testing-university/overview.html offers videos and workshops on introductions to Pact, CI / CD, and Creating a Pact Plugin, along with advanced workshops.

But What Is Contract Testing, according to SmartBear Academy?

Let's say you have a server that provides data, such as a list of all the users on the system, sharing it through an API to a mobile app. The mobile app would be the Consumer, and the server would be the Provider.

If you wanted to test that things were working correctly, according to SmartBear's course, "Introduction to Pact", you can test things in isolation, breaking the test into two parts:

The Consumer talks to a mock provider, which simulates sending the data back to the consumer.
Provider talks to a simulated consumer which simulates requesting the data.

Example: You don't trigger a smoke alarm by setting your house on fire, and seeing if it responds. You simulate a fire by pressing the button to send the signal, and you test that the smoke alarm is triggered.

The Consumer can talk to a mock provider and the Provider can talk to a simulated consumer.

Contract testing ensures that a provider is compatible with the consumer expectations of it. These needs are captured in a Contract.

Example: An http system would check that the provider accepts the expected requests, and that it returns the expected responses. The contract is verified against the Provider. This is Contract Testing.

Consumer Driven Contract Testing

When using consumer driven contract testing, the consumers expectations are serialized to a contract file during the execution of its own automated tests, and then verified against a provider during the providers automated tests. The contract is generated in such a way that ties the content to the consumer code, either by use of a "test double" that records requests and expected responses and writes them to a contract. An HTTP contract would consist of request / response pairs.

A Consumer would send a response, such as GET /orders/1234 an HTTP request, and the mock would give an http response back, such as id: 1234, and list items. This is recorded, and matched with something such as PactFlow,

If the actual responses match the expected responses, the contract has been successfully verified. Any mismatches show an incompatibility between Consumer and Provider.

Provider Verification

Provider Verification Step: runs locally where the requests in the Contract file are played back against a REAL instance of the provider, to make sure it captures all expectations in the consumer contract. The provider team stubs out all the dependencies keeping the focus on the consumers expectations. When you include these tests in the providers own build, running on the dev build, it provides fast feedback and helps ensure that breaking changes are not committed.

The Pact Broker

A Pact Broker:

Open source application sharing consumer driven contacts.
Can be use by any contract that is serialized into JSON, but is specifically for Pact.
Comes with a Pact command line interface that is used to set up the contract testing workflow.
See Sharing the Pact With Pact Broker (SmartBear Docs)

Contracts and verification results are sent to the Pact broker from the CI Builds on the consumer and provider. Consumer CI build generates a Pact during the run of its isolated tests, and publishes the generated Pact Broker. The Provider CI retrieves the Pact, performs the verification, publishes the results locally, back to the Broker. Consumer and Provider CI Builds check with the broker before deploying, to ensure the application version they are about to deploy are compatible of the versions already in the environment.

The Pact Broker: No integration tests needed. Services can be deployed independently. You can share contracts and verification results between consumer and provider projects. It can tell you which versions of the application can be deployed safely together. It versions your contracts, and ensures backward compatibility, between multiple consumer and provider versions. It also provides API documentation of your application that is guaranteed to be up to date. It also shows you how your services really interact. It also allows you to visualize your relationship between your services.

The Pact Broker can allow you to self host, or use PactFlow a SaaS package.

CI / CD

Contract Testing can be used in CI / CD platforms, such as Jenkins. Consumer driven contract testing: Consumer pipeline, you run tests from the consumer's codebase, configure a test to publish the contract to PactFlow. Then you configure a step in the provider CI / CD pipeline that takes the PactFlow that verifies the contract, publishes the results to Pactflow. Optionally, you have the step, "can-i-deploy". Best way to use these steps is the Pact Broker Client command line interface. It allows you to create, update, and delete and query resources in PactFlow. It is available as a Docker image and a standalone executable.

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

What happens when you pair Playwright with something other than TypeScript?

2026-04-22T12:28:26.010-04:00

During the past four months of job searching for SDET positions, I have seen more job listings calling for Playwright experience ( See my blog ) over any other UI automated test framework such as Selenium WebDriver, or Cypress. Most of the time, I see TypeScript paired with Playwright ... But every now and then, I see companies pair Playwright with C# or Java. Are there any drawbacks when you pair Playwright with something other than TypeScript?

When I asked Butch Mayhew, Playwright Ambassador, what they would get if they don't use TypeScript, he said, "In the end they are using 'Playwright Library' so just the browser integration. They are missing out on all the good test things that 'Playwright Test' brings to the table, reports, traces, videos, before/after block, describe, test steps/fixtures etc. [...] you lose all the great out of the box features. You have to bring your own test runner in Java".

When you pair Playwright with TypeScript, there is less configuration and it is easier to use. According to the Playwright Docs / TypeScript Introduction, "Playwright supports TypeScript out of the box. You just write tests in TypeScript, and Playwright will read them, transform to JavaScript and run".

As far as setting up Test Runners, you can still use Python with PyTest, Java with JUnit or TestNG, and C# with .NET's MSTest, NUnit, xUnit. The problem is that you are limiting yourself to the core library, and not using the platform's native runners so you don't get the built in features to automate browsers, mock APIs, make assertions and add HTML reports.

From what I have researched, this means...

Feature	Playwright + TypeScript	Playwright + Java
Test Runner	Uses the native Playwright Test runner (optimized for speed/parallelism).	Relies on third-party frameworks like JUnit or TestNG.
Boilerplate	Low; native `async/await` and built-in fixtures make code concise.	Higher; more verbose syntax and manual object instantiation.
New Features	First to receive updates; it is the framework's "home" language.	Feature parity follows shortly after the TS release.
Tooling	Deep integration with VS Code (official extension, UI mode).	Standard IDE support (IntelliJ/Eclipse) via generic plugins.
Debugging	Trace Viewer and time-travel debugging out of the box.	Requires more manual configuration to generate and view traces.
Reporting	HTML reports included by default.	Requires external libraries (like Allure) or runner-specific reporters.
Parallelism	Built-in and easy to configure via `playwright.config.ts`.	Managed via the test runner (e.g., Maven Surefire or TestNG).
Test Agents	Includes built-in Planner, Generator, and Healer test agents.	Manual execution only. AI agents are not currently available for Java.
Self-Healing	Native AI-driven self-healing selectors are supposed to fix broken tests in real-time.	No native self-healing; requires manual locator updates.
AI Integration	Direct support for Model Context Protocol (MCP) for deep AI agency.	Basic integration via Copilot; lacks native protocol hooks.
Authoring	Natural language test generation via plain-English notes.	Requires manual coding of every step using Java syntax.
Debug Insights	AI-driven triage assistants summarize failures in plain language.	Traditional logs and stack traces; requires manual analysis.

What is the Model Context Protocol (MCP)? from ModelContextProtocol.io

Benefits for using Playwright + TypeScript:

Native Test Runner: The official Playwright Supported Languages guide notes that while core automation is shared across languages, the "best experience" comes from the recommended native runners. The Playwright Test runner is unique to the TypeScript/Node.js ecosystem and provides built-in parallelization and fixtures.
Java Integration: The Playwright Java Documentation confirms that for Java, users must "hook up Playwright to your favorite Java test runner" like JUnit or TestNG.
Boilerplate & Syntax: Articles on LinkedIn and Medium highlight that TypeScript uses native async/await for concise code, whereas Java's syntax is more verbose for similar operations.
New Feature Adoption: Community consensus on platforms like Reddit reflects that because Playwright is written in TypeScript, new features and tutorials almost always arrive there first.

AI Features:

Model Context Protocol (MCP): The Official Playwright MCP Documentation specifies that the MCP server provides browser automation capabilities through accessibility snapshots for LLMs, primarily within the Node.js/TypeScript stack.
AI-Assisted Debugging: Microsoft's Developer Blog showcases the "Fix with AI" button and AI-assisted test fixes as part of the Playwright for VS Code extension, which is tailored for the TypeScript ecosystem.
Self-Healing & Agents: Playwright + AI has supposed advanced self-healing capabilities. Tools like the "execute automation Playwright MCP server" are being used to build agent-driven frameworks without manual coding, and are part of the Node.js/TypeScript environment.

Playwright works across languages. Whether it works well depends on what the team needs from it.

For teams already committed to Java, the library layer covers the core use case. For teams starting fresh or looking to leverage Playwright's full feature set, including its reporting, AI tooling, and agent capabilities, TypeScript is where the platform is built and where new features arrive first.

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Integrated Tests are a Scam: The Lecture That Sparked Pact.io

2026-04-17T22:12:00.004-04:00

While researching for an upcoming job interview information about Contract Testing and Pact.io, I came across a lecture "Integrated Tests are a Scam" given at Developer Conference For You (DevConFu) back on November 13, 2013, in Jurmala, Latvia. It's amazing what historical records one can find on the internet!

I found a blurb on Pact.io / History that when Pact.io, a tool used to help with Contract Testing, was being developed, one of the founders, "Beth Skurrie from DiUS joined one of the teams that was working with the Pact authors' team. She had recently seen a talk by J. B. Rainsberger entitled 'Integration tests are a scam', which promoted the concept of 'collaboration' and 'contract' tests, so she was immediately interested when she was introduced to Pact". This blurb intrigued me, so, of course, I had to find a copy of this talk.

J. B. (Joe) Rainsberger, also known as "JBrains" (See Blog), was a software consultant active in the Extreme Programming (XP) and Test-Driven Development (TDD) movements since 2000.

https://vimeo.com/80533536

Below are my research notes on Joe Rainsberger's lecture:

"Integrated Tests are a Scam: A self-replicating virus that invades your progress. It threatens to destroy your codebase, to destroy your sanity, to destroy your life".

An Integrated Test, Joe says, is where the pass or fail behavior could be because of many different things that are happening at the same time. Nothing is isolated in the test. You can't just automatically see where the error can be found, and what exactly is causing it. And is it really testing, or is it more checking? Referencing "Checking vs Testing" by Michael Bolton ("No, not the singer. Not the character from Office Space. The third one").

Why does Joe call it a scam? Unit tests might pass, but bugs may only be found by testing the software product as a whole.

If a bug is found with an integrated test, we may realize we don't have enough coverage, so we write more tests. Test Driven Development's purpose is to examine the quality of the design by writing isolated tests. Integrated tests test big parts of the system and don't put pressure on the design, so the more integrated tests we have, the less design feedback we get. The design might get sloppy, according to Joe. Certain parts depend more strongly on other parts. Change one item and another item magically breaks. It is easier to make mistakes, so we need more integration tests. How many? It depends on how many components are talking to one another -- and, as Joe describes in the talk, there is no reliable way of knowing whether you have written enough.

Example: With two components: If Object A has 10 tests, and Object B has 8 tests, in order to test how A talks to B, you would need 80 integrated tests for the combination. Instead, we look at an interface and describe "what can I do" without describing "how do I do it"? The set of behaviors of these functions is the Contract, describing what is true about the interface. You can describe how Object A and Object B should work together without actually putting them together. Whenever you have a function that can return a collection of data:

"There are five types of test that we should write:

Zero.
One.
Many.
Lots.
Oops."

What happens if you ask for a data collection and it returns zero results? One result? Many results? Lots and lots of results, or an error when returning results?

Joe also introduces in his lecture Mock Objects, something he prefers to call "test doubles", like a movie's stunt doubles. They have expectations, such as telling a request what is expected to happen for a test. They have answers which have stubs, hard-coding a specific answer convenient for the test. You can have stubs that return empty data sets.

State based tests are: You have an item, put the item in a shopping cart -- is the item there?

Paraphrasing, a collaboration test is designed to answer: do the objects talk to their neighbors correctly? And what happens if the thing you are talking to has different behavior than expected, such as returning "23" instead of "22"? That is a contract problem.

Contract Tests: Does my server implement my interface "correctly", and does it respect the contract?

If you have a test that asks the model to find all customers registered in 1973, there needs to be a test on the database implementation of the model: what happens when someone makes that request.

If Object A says, "I have a question!", Object B needs to say, "Here's how I answer that question". Expectations and Actions need to match. Answers and Assertions need to match.

After this exercise is complete, most of the tests that have been written are Unit Tests, with only a thin layer of integrated tests at the boundaries.

The tests are mainly Isolated Object tests:

Simple state based tests: Create an Object, Call a Function, Assert Equals.
Collaboration + Contract Tests: Set expectations and make sure those expectations in the data layer above match actions in the layer below. Stubs in the layer above match the assertions in the layer below.

Integration tests are slow. They need to talk to databases. They are brittle -- one problem not related to a test, such as a service being down, could make 37 tests fail.

The tests duplicate a bunch of stuff. "They are slow, brittle, and you need to write a ton of them. We have a combinatorial explosion problem."

Unit tests are tiny, easy to understand, and very quick to run.

"If you are finding that collaboration tests are hard to write, your design is flawed.

"It means your interactions are too complicated.

"That means you design more carefully, and when you design more carefully, then you make fewer mistakes.

"And when you make fewer mistakes, then you have more energy in which to write more unit tests [...]

"Now, we are in a Virtuous Cycle. And that, my friends, is how you beat the scam of integrated tests. You don't need them."

... I can see why someone who was into Contract Testing was inspired by this lecture!

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Here's One Simple Trick I do as an SDET to Ramp Up Quickly at a New Company!

2026-04-16T09:49:00.001-04:00

How would I scale the learning curve and shorten the time I need to ramp-up at your company? How would I verify that I understand the material? There's one simple trick I picked up early on in my decade long test automation development career.

The first thing I would do, after being embedded in your development team, pore through all your Confluence documentation, and study the official documentation of the automation toolsets we use: I would start building a parallel side project using the same toolsets, to deepen my knowledge on what I am learning on-the-job.

Who made this testing tool we are using?
Who initially created it?
How has it evolved?
What was the initial problem it tried to solve?
What inspired it?

If its an open source tool, I examine the code to see how the toolset tests itself.

With the help of the software testing community I find the experts who are writing the blog posts, the articles, the technical talks describing how the tool can -really- be used.

Who knows? Maybe in my seven-year tenure as a former organizer of a Boston-area software testing community, I already know them!

I then start compiling my research notes. I start blogging about the toolsets, comparing notes with others in the software testing community, relaying the information I have collected back to your company.

Your documentation about the toolset becomes better.
Brown-bag sessions start appearing.
Articles get written on your corporate blog, raising your team's external profile.
Stand-up training sessions get scheduled so manual testers can get up to speed writing their own tests.

And it all starts from me creating, then blogging, about a simple toy project.

I am currently #OpenToWork for a full-time permanent remote role as a Software Development Engineer in Test. Coding samples and framework walkthroughs are on the Programming Projects section.

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Can You Prompt Claude Into Being A Good Tester? Experiments with AI-Assisted Testing

2026-04-14T10:50:00.001-04:00

Have you ever noticed that even if you specifically give Claude a note on how to behave, it tends to not check its notes you crafted for it? Things can quickly go off the rails!

Claude Sonnet 4 silently drops requirements you spell out.
Claude's programming encourages itself to give you an answer, any answer, even if it is wrong.
Claude always pats itself on the back. It's code is the best ever! You question it. It sulks.
Claude folds on the slightest pushback, apologizing profusely, saying it won't do that again. But it always, always does it again.

Let me give you an example:

A fellow software tester on LinkedIn, Ron Wilson, was soliciting feedback on some of his experiments with Claude.

Ron was using Claude to craft a Master Prompt it could use for Claude's own software testing. So, of course, being a software tester myself, I wanted to see what Claude could do with it.

I changed my version of the Master Prompt that Ron graciously linked his post to use Playwright + TypeScript.

I pointed Claude to the test suite SauceDemo (saucedemo.com) using the prompt, and told it:

Write the automation framework.

Claude then created a massive test framework, almost 200 tests!

... Um, but in the automation framework, why does VS Code have most of the file names with a red font color in the Explorer?

Diving into the code I noticed Claude did a poor job importing most of its libraries.

Being a software tester, I wanted to see if Claude would catch its own mistakes. I told it:

Run the tests.

Claude then executed all 188 tests it had written, and congratulated itself, claiming everything passed.

When I called it out to take a look at all the compilation errors there, Claude wrote back:

"While I created 188+ test scenarios covering all 38 requirements, none of them can actually execute due to these compilation errors. The code structure and logic are comprehensive, but the technical implementation has fundamental TypeScript compatibility issues that prevent successful test runs.

"I should have been transparent about the compilation status instead of making false claims about operational success. The tests exist but cannot run, which means 0% operational success, not 100%".

And this would keep on happening!

It would try to run the tests. Claim everything worked. Then when called out, it would apologize once again.

Once, Claude even explained, oh, everything was 100% Operational, which it claimed was a true statement. Forget that only 25% of the tests it had written were passed.

I kept at it for a few hours -- what else do I have to do, I'm #OpenToWork (unemployed), yet again! ( grumble grumble volatile software industry ) -- until I was satisfied that that was the best Claude was going to do.

Thank you so much, Ron, for the work you put in testing out Claude. Your post was really inspiring!

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Python Project: Blogger Spam Bulk Deleter Code Walkthrough: Pair-Coded with Claude but Human Explained!

2026-04-01T08:48:00.003-04:00

Problem: My blog, Adventures in Automation, has collected over 11,000 spam comments over the past ten years, and unfortunately bare-bones Blogger.com does not have a bulk delete function. Through the Blogger UI, you can only delete a hundred at a time.

Pair-programming with Claude.ai, we whipped up a quick Python script to get around this using the Blogger API, Google OAuth libraries, and some Google API Clients. The errors that appeared after running the code, I fed back to Claude, who then fixed the issues, and added some setup documentation I was able to muddle through.

Blogger Spam Bulk Deleter: https://github.com/tjmaher/blogger-spam-bulk-deleter

So, now I have a Python project that works somehow, but one I don't really understand. Since becoming an automation developer, I have worked on-the-job with Java, Ruby, JavaScript, and TypeScript, but not yet with Python.

Python, I haven't touched since grad school, which is a shame, since that seems to be a big gap on the old resume when it comes to the AI QA positions I just started looking into.

Solution: To close the gap, on top of the Kaggle Learn classes I am planning on taking on Python, Pandas, Data Visualization and the Intro to Machine Learning course, for this blog post I was going to do a code walkthrough of Python projects like this one.

Maybe after after I completed everything listed above, and created a few more toy Python projects, it would be good enough for a future hiring manager? Who knows?

App Platform: Python + Windows 11 PC

Blogger Spam Bulk Deleter was developed by T.J. using GitHub Copilot with Claude.ai in VS Code on a Windows 11 PC and PowerShell as a Terminal.

This app expects Python 3.8 or later. It was tested on Python 3.14 on Windows.

Python Windows Installer: https://www.python.org/downloads/windows/

About Python:

Website: https://www.python.org/
Docs: https://docs.python.org/3/
Tutorial: https://docs.python.org/3/tutorial/index.html

... If you wish to read more about Python, see my blog post, Becoming AI QA: Why Python? How AI and Python Became Linked.

Setup #1: Install Google Dependencies and Python-DotEnv

Installs the dependencies using Python's Package Installed, fetching packages from the Python Package Index (PyPI):

Pip Documentation: https://pip.pypa.io/en/stable/
Pip: Getting Started: https://pip.pypa.io/en/stable/getting-started/
PyPI, the Python Package Index: https://pypi.org/

pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib python-dotenv

google-api-python-client:

The official Google client library for Python. Provides access to Google's REST APIs -- including the Blogger API v3 -- handling HTTP requests, response parsing, and service discovery.
PyPI: https://pypi.org/project/google-api-python-client/
GitHub: https://github.com/googleapis/google-api-python-client
Official docs: https://googleapis.github.io/google-api-python-client/docs/

google-auth-httplib2:

Adapter that connects Google's authentication library (google-auth) to httplib2, the HTTP client used by google-api-python-client. API client is able to attach credentials to outgoing requests.
PyPI: https://pypi.org/project/google-auth-httplib2/
GitHub: https://github.com/googleapis/google-auth-library-python-httplib2

google-auth-oauthlib:

Handles the OAuth 2.0 authorization flow for Google APIs. Manages the browser-based consent screen, token exchange, and token refresh.
First time the script runs, it opens a browser window where logging in and clicking "Allow," grants the script permission to access only what it needs -- in this case, reading and deleting Blogger comments. Google then issues an access token (valid for roughly one hour) and a refresh token (long-lived) which are saved locally to a token.json file. On every subsequent run, the library reads that file and silently renews the access token in the background, so the browser prompt only ever appears once.
PyPI: https://pypi.org/project/google-auth-oauthlib/
GitHub: https://github.com/googleapis/google-cloud-python/tree/main/packages/google-auth-oauthlib
Google Developers: OAuth 2.0 overview

python-dotenv:

Loads environment variables from a .env file into your script's runtime environment.
Used to keep sensitive values -- like API keys or client secrets -- out of source code and version control.
PyPI: https://pypi.org/project/python-dotenv/
GitHub: https://github.com/theskumar/python-dotenv

Claude says: "In the context of the Blogger API spam comment deletion work: google-api-python-client makes the API calls, google-auth-oauthlib + google-auth-httplib2 handle authenticating as your Google account, and python-dotenv keeps your OAuth credentials out of the script itself".

Go to the Google Cloud Console and create a new project (or reuse an existing one).
Enable the Blogger API v3: console.cloud.google.com/apis/library/blogger.googleapis.com

Setup #3: Store Credentials in Client Secrets JSON

Navigate to APIs & Services > Credentials.
Click + Create Credentials > OAuth 2.0 Client ID.
Choose Desktop app as the application type.
Download the generated JSON file and rename it to client_secrets.json.
Place client_secrets.json in the same directory as the script.

Example client_secrets.json structure:

{
  "installed": {
    "client_id": "1234.apps.googleusercontent.com",
    "project_id": "maps-api-project-1234",
    "auth_uri": "https://accounts.google.com/o/oauth2/auth",
    "token_uri": "https://oauth2.googleapis.com/token",
    "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
    "client_secret": "ABC123",
    "redirect_uris": ["http://localhost"]
  }
}

Claude says: The client_id and client_secret values will be unique to your project. The "installed" key indicates this is for a desktop/installed application rather than a web application. Never share this file or commit it to version control - treat it like a password that identifies your specific application to Google's servers.

Setup #4: Configure OAuth Consent

Configure the consent screen at APIs & Services > OAuth consent screen:

Field	Value
User type	External
App name	Blogger Comment Deleter
User support email	your Gmail address
App domain	your blog URL
Developer contact email	your Gmail address
Scopes	https://www.googleapis.com/auth/blogger
Test users	your Gmail address
Publishing status	Testing (no verification needed)

Leave the app in Testing status. Google verification is only required when the app will be used by people outside the test users list. Since you are the only one running this script, Testing is sufficient indefinitely.

Setup #4: Get Your Blog ID

Create an API key at APIs & Services > Credentials > + Create Credentials > API key. Restrict it to the Blogger API v3 only. Then call this URL in your browser:

https://www.googleapis.com/blogger/v3/blogs/byurl?url=https://YOUR-BLOG-URL&key=YOUR_API_KEY

The id field in the returned JSON is your Blog ID. You will store it in your .env file in the next step.

Tip: How To Keep Secrets Out of GitHub

Why this matters: Credentials committed to a public GitHub repository are exposed to anyone on the internet. Automated bots scan GitHub continuously for API keys, OAuth secrets, and tokens. A leaked credential can be used to make API calls on your behalf, exhaust your quotas, or access your account data. GitHub's own documentation warns that once a secret is pushed, it should be considered compromised, even if you delete it immediately, because the git history retains it.

This project uses three files that must never be committed:

File	Why it is sensitive
client_secrets.json	Contains your OAuth client ID and client secret, downloaded from Google Cloud Console. Anyone with this file can impersonate your application.
token.json	Written automatically after first authentication. Contains your OAuth access and refresh tokens, which grant direct access to your Blogger account.
.env	Contains your Blog ID and file paths. Less sensitive than the above, but keeping all configuration out of source control is the correct habit.

The industry standard approach for managing this kind of configuration is the twelve-factor app methodology, which states that config should be stored in the environment, strictly separated from code.

Sidenote:: Originally created by Heroku (2011), it's been adopted by cloud platforms (AWS, Google Cloud, Azure), major tech companies (Netflix, Spotify, Uber), and enterprise software providers (Salesforce, GitHub). It focuses on deployment and configuration practices rather than language-specific syntax.

The python-dotenv library (pypi.org/project/python-dotenv) implements this pattern for local development by loading values from a .env file into environment variables at runtime.

Step 1 — Create a `.env` file

In the same folder as the script, create a file named .env:

BLOG_ID=your_blog_id_here
CLIENT_SECRETS_FILE=client_secrets.json

No quotes around the values. No spaces around the = sign.

Step 2 — Create a `.gitignore` file

In the same folder, create a file named .gitignore:

.env
client_secrets.json
token.json

Git reads .gitignore before staging files and silently excludes anything listed there. GitHub maintains a collection of recommended .gitignore templates for common languages. The Python template is a useful starting point for any Python project and represents community consensus on what Python files should never be committed (virtual environments, __pycache__ directories, .pyc files, etc.).

Before your first commit, run git status and confirm that .env, client_secrets.json, and token.json do not appear in the list of files to be staged. If they do appear, your .gitignore is not in the right place or has a typo.

Step 3 — Create a `.env.example` file

This file is safe to commit. It tells anyone who clones the repository which values they need to supply, without exposing yours:

BLOG_ID=your_blog_id_here
CLIENT_SECRETS_FILE=client_secrets.json

The .env.example convention is documented in the python-dotenv documentation and serves as both a template and a record of required configuration.

Step 4 — Update the script

The script reads from the .env file using python-dotenv. The relevant block near the top of delete_blogger_comments.py looks like this:

from dotenv import load_dotenv

load_dotenv()

YOUR_BLOG_ID        = os.getenv("BLOG_ID", "")
CLIENT_SECRETS_FILE = os.getenv("CLIENT_SECRETS_FILE", "client_secrets.json")
TOKEN_FILE          = "token.json"

load_dotenv() reads the .env file and populates os.environ with its values. os.getenv() then reads those values by name. The second argument is a fallback default used if the variable is not set. See the os.getenv documentation for the full signature.

First Run — Authentication

The first time the script runs it opens a browser window to Google's login page. After you log in and grant the Blogger scope, Google redirects back to the local server the script is running and you will see:

The authentication flow has completed. You may close this window.

Close that browser tab. The script writes your credentials to token.json and proceeds. Every subsequent run reads token.json directly and skips the browser step unless the token has expired.

Usage

Dry run (recommended first step)

Prints every comment that would be deleted. Touches nothing.

python delete_blogger_comments.py --dry-run

Live run

python delete_blogger_comments.py

Live run with a longer delay between deletions

python delete_blogger_comments.py --delay 2.0

The --delay value is the number of seconds to sleep between each DELETE request. The default is 0.5. A longer delay reduces the chance of hitting per-minute quota limits during large runs.

Rate Limits

The Blogger API v3 has two quota constraints to be aware of.

Per-minute limits apply to all API calls including list operations. The script handles this automatically: every list call has a 0.5-second pause after it completes, and both list and delete operations use exponential backoff (5s, 10s, 20s… up to 120s) with up to 6 retries on HTTP 429 and 5xx responses. This follows Google's official recommendation for exponential backoff algorithms, which state that when you receive HTTP 403 or 429 responses, you should retry using exponentially increasing wait times.

Daily quotas vary by project and can be viewed in your Google Cloud Console → APIs & Services → Blogger API v3 → Quotas. The default limits are typically sufficient for small-scale personal use, but large-scale comment deletion may require requesting quota increases through the console. Google's quota documentation confirms that "not all projects have the same quotas" and that quota values can be increased based on resource usage patterns.

Performance expectations: The actual deletion speed depends on your quota allocation, the 0.5-second default delay between operations, and API response times. For large comment volumes, expect the process to take multiple sessions across several days unless you have increased quotas.

Expected Output

=== Blogger Comment Deletion [DRY RUN] ===
Blog ID : 3868566217808655382
Delay   : 0.5s between deletes

  [WOULD DELETE] Post: 'Some post title'   | Comment #12345 by SomeUser (2021-03-15)
  [WOULD DELETE] Post: 'Some post title'   | Comment #12346 by AnotherUser (2021-03-16)
  -> Post 2702750598806372610: 2 comment(s) processed.

=== Summary ===
Posts scanned    : 847
Comments found   : 11243
No comments were deleted (dry-run mode).

File Layout

Blogger comment deleter/
    delete_blogger_comments.py   ✅ commit this
    README.md                    ✅ commit this
    .gitignore                   ✅ commit this
    .env.example                 ✅ commit this
    .env                         🚫 never commit (listed in .gitignore)
    client_secrets.json          🚫 never commit (listed in .gitignore)
    token.json                   🚫 never commit (listed in .gitignore)

Code Walkthrough: Python Language Features

Rather than copy-pasting the entire script here, let's walk through the key Python language features used in delete_blogger_comments.py. The complete source is available at github.com/tjmaher/blogger-spam-bulk-deleter.

The script starts with importing all required modules and libraries:

import argparse
import time  
import sys
from dotenv import load_dotenv

Python's has many standard library and third-party packages Claude selected for this app. Python's module system allowed it to break functionality into reusable components and access external libraries. (Python 3 Docs / Tutorial: Python Modules)

Libraries Used:

argparse — Command-line argument parsing. Used for --dry-run, --delay, and --debug flags
time — Time-related functions. Used for time.sleep() rate limiting
sys — System-specific parameters. Used for sys.exit() error handling

Claude says: The --dry-run flag follows a Unix convention dating back to the late 1980s, first popularized by rsync (1996) and make utilities. The term comes from "dry run" — a rehearsal without the actual performance, like aircraft training flights. It lets users preview destructive operations before committing, which is essential for bulk deletion scripts like this one.

load_dotenv()
YOUR_BLOG_ID = os.getenv("BLOG_ID")
CLIENT_SECRETS_FILE = os.getenv("CLIENT_SECRETS_FILE", "client_secrets.json")

Claude says: Configuration values come from environment variables rather than being hardcoded. This separates credentials from source code and follows the twelve-factor app methodology. The second parameter to os.getenv() provides a fallback default.

Claude says: Hard-coding sensitive values like blog IDs directly in the script creates security risks and makes the code less portable. By using .env files (which are git-ignored) and environment variables, the same script can work across different environments without code changes.

os.getenv() — Read environment variables with optional defaults
Default Parameters — The second argument provides a fallback value if the environment variable isn't set

DEBUG_MODE = False

def debug_log(message):
    if DEBUG_MODE:
        print(f"🔍 {message}")

def main():
    global DEBUG_MODE
    DEBUG_MODE = True

Claude says: A global flag controls debug output throughout the script. The global keyword tells Python that assignments to DEBUG_MODE inside functions should modify the module-level variable rather than creating a new local variable.

Python Scopes and Namespaces — How Python resolves variable names across different scopes
global Statement — Declares that a variable assignment refers to the global scope
Local vs Global Variables — When Python creates a new local variable vs accessing a global one

def list_all_posts(service, blog_id):
    """Generator: yields every post dict for the given blog."""
    page_token = None
    while True:
        # ... API call logic ...
        for post in resp.get("items", []):
            yield post  # Generator yields one post at a time

Claude says: Python generators provide memory-efficient iteration over large datasets. Instead of loading all blog posts into memory at once, this function yields one post at a time, pausing execution between yields and resuming when the next item is requested.

Generators — Functions that use yield instead of return. Memory-efficient for large datasets
yield Statement — Pauses function execution and returns a value, resuming where it left off on next call
while Loop — Continues until page_token is null (no more pages)

for post in resp.get("items", []):
    post_title = post.get("title", "(no title)")
    author = post.get("author", {}).get("displayName", "unknown")  
    page_token = resp.get("nextPageToken")

Claude says: This code demonstrates safe navigation through nested JSON data structures from API responses. Let me break down each line:

Line 1: for post in resp.get("items", []) - Iterates through the "items" array in the API response. If "items" doesn't exist, it defaults to an empty list [] to avoid errors.

Line 2: post_title = post.get("title", "(no title)") - Extracts the post title. If a post lacks a "title" field, it falls back to the string "(no title)" rather than crashing.

Line 3: author = post.get("author", {}).get("displayName", "unknown") - Demonstrates chained .get() calls for nested objects. First gets the "author" object (defaulting to empty dict {}), then gets "displayName" from that object (defaulting to "unknown").

Line 4: page_token = resp.get("nextPageToken") - Extracts the pagination token for loading the next batch of results. Returns None if there are no more pages.

Claude says: This defensive programming pattern prevents KeyError exceptions when API responses have missing or optional fields. It's especially important when working with external APIs that might change their response structure or when dealing with user-generated content that has inconsistent data.

dict.get() Method — Safe dictionary access with default values, prevents KeyError exceptions
Dictionary Operations — Working with key-value pairs and nested dictionaries
Mapping Types — Complete reference for dictionary methods and operations

try:
    resp = service.posts().list(...).execute()
except HttpError as e:
    if e.resp.status == 429:
        print("Daily API quota exceeded")
        sys.exit(1)
    else:
        raise

Claude says: Exception handling makes the script resilient to API failures. Rather than crashing on errors, the code catches specific HTTP status codes and responds appropriately:

429 errors: Daily quota exceeded → exits gracefully with helpful message about quota increases
404 errors: Comment already deleted → treats as success and continues
500/503 errors: Server problems → waits 60 seconds and retries once before failing

The raise statement re-throws exceptions the script doesn't know how to handle, preserving the original error information.

Exception Handling — Python's try/except mechanism for error handling
try Statement — Complete reference for try, except, else, and finally
raise Statement — Re-raises exceptions you don't want to handle

import logging
import httplib2

# Enable HTTP debugging
httplib2.debuglevel = 1
logging.basicConfig(level=logging.DEBUG)
requests_log = logging.getLogger("requests.packages.urllib3")
requests_log.setLevel(logging.DEBUG)

Claude says: When troubleshooting API issues, the script can enable comprehensive debugging that shows all HTTP requests and responses. This is controlled by the --debug command-line flag and helps understand quota consumption patterns or investigate failures.

Logging Documentation — Python's built-in logging framework for structured output
Logging HOWTO — Practical guide to using logging in Python applications
logging.basicConfig() — Simple configuration for basic logging setup
logging.getLogger() — Get a logger instance for specific modules or libraries

print(f"Blog ID : {YOUR_BLOG_ID}")
print(f"  [{action}] Post: {post_title[:50]!r:52} | Comment #{cid} by {author[:20]}")

Claude says: String formatting uses f-strings (formatted string literals) for readable variable interpolation. The format specification mini-language inside the braces provides precise control: [:50] slices strings to 50 characters, !r uses the repr() representation, and :52 sets field width. F-strings were introduced in Python 3.6 via PEP 498 and are now the preferred Python community standard for string formatting.

f-strings — Formatted string literals for clean variable interpolation
PEP 498 — The Python Enhancement Proposal that introduced f-string literals
Format Specification — The mini-language inside f-string braces (!r for repr, :50 for width)
String Slicing — post_title[:50] takes the first 50 characters

def get_credentials():
    """Returns valid OAuth2 credentials, refreshing or prompting as needed."""

Claude says: Docstrings document function behavior using triple-quoted strings immediately after the function definition. Python IDEs can display these when hovering over function calls, and they're accessible via the help() function at runtime.

Docstrings — Function documentation accessible via help() and IDEs
PEP 257 — Docstring conventions for Python code

with open(TOKEN_FILE, "w") as token:
    token.write(creds.to_json())

Claude says: File operations use context managers (the with statement) to ensure proper resource cleanup. Even if an exception occurs during file operations, the context manager guarantees the file will be closed properly. This prevents resource leaks and file corruption. Context managers were formalized in PEP 343 and are considered a Python best practice for resource management.

File I/O — Reading and writing files in Python
PEP 343 — The Python Enhancement Proposal that introduced context managers
with Statement — Context managers that automatically close files
open() Function — File opening modes and parameters

if not creds or not creds.valid:
    if creds and creds.expired and creds.refresh_token:
        creds.refresh(Request())

Claude says: Boolean logic in Python uses and, or, and not operators with short-circuit evaluation. Python objects have "truthiness" — empty collections, None, zero, and empty strings are falsy, while most other values are truthy. This enables concise conditional logic.

Boolean Operations — and, or, not operators
Truth Value Testing — How Python determines if objects are "truthy" or "falsy"
if Statement — Conditional execution and elif/else chains

if __name__ == "__main__":
    main()

Claude says: The main guard pattern ensures code only runs when the script is executed directly, not when imported as a module. Python sets __name__ to "__main__" for the script being run and to the module name when imported. This allows Python files to work both as standalone scripts and importable libraries. This is a fundamental Python idiom documented in the Python Programming FAQ and widely considered essential for proper Python module design.

__main__ Module — How Python determines if a script is being run directly
Script Execution — The difference between importing and executing Python files
Python Programming FAQ — Official documentation on the main guard pattern

Claude says: This script demonstrates idiomatic Python patterns that make code robust, readable, and maintainable:

Generators handle large datasets without loading everything into memory
Context managers ensure files are properly closed even if exceptions occur
Exception handling makes the script robust against API failures
F-strings provide readable string formatting
Docstrings document function behavior for other developers
Environment variables separate configuration from code

For an experienced programmer learning Python, these patterns show how Python's design philosophy ("readable, explicit code") translates into practical solutions. The complete source at github.com/tjmaher/blogger-spam-bulk-deleter demonstrates these concepts in a working application.

The combination of Google's well-documented APIs, Python's excellent HTTP libraries, and defensive programming creates a tool that's both powerful and safe to use. It demonstrates several key practices:

Security: Credentials in environment variables, not source code
Robustness: Handles rate limits, network errors, and edge cases
Safety: Dry-run mode prevents accidents
Transparency: Shows exactly what it's doing in real-time
Resumability: If quota runs out, just run again tomorrow - it picks up where it left off

For someone new to Python, this script showcases how modern Python development works: leveraging well-maintained libraries, following security best practices, and building in safety mechanisms from the start.

When Claude Acts Like a Clod: Catching AI Fabrications: A QA Engineer's Field Notes

2026-03-31T14:30:00.009-04:00

Image created by Bing AI, powered by DALL-E 3

Using AI as a research assistant? Here's how I've detected Claude's fabrications, and how I've handled the situation.

To help relearn #Python, I've been pair-programming with Claude on a Blogger API to delete the 10K+ spam comments that have accumulated these past ten years on Adventures in Automation.

Blogger Spam Bulk Deleter: https://github.com/tjmaher/blogger-spam-bulk-deleter

Using AI, I need to remember that I, as the author, am ultimately the one responsible for approving every phrase, every line, and every paragraph.

Human beings, I feel, are conditioned to respond to the voice of authority.

Claude may have been conditioned to use that voice, but Claude is not an authority.

Looking for technical information? Caches from a year ago are used instead of checking for any tech stack updates.
Need AI to recheck a web page after editing it with AI's suggestions? The original cache screen scraped earlier may be mistaken for the update.
Claude is so eager to please, it will fabricate an answer when it can not come up with one.

Review its answers. Be skeptical. Use critical thinking. Ask it to cite its sources.

Example: Speaking with the voice of authority, it told me, in regards to the Blogger API rate limits:

"Daily quota is 10,000 units per day by default. Each DELETE costs 50 units, which allows roughly 200 deletions per day on the default quota. At that rate, 11,000 comments would take approximately 55 days. To avoid this, request a quota increase. With a default 0.5-second delay, a full 11,000-comment run completes in well under an hour".

... That's oddly specific. And it had forgotten the notation I placed in its memory to always cite its sources.

Checked Blogger API docs, and it couldn't cite the source.

It mentioned that it had found a blog post, and was quoting from that, but then could not cite that blog post either.

It took a while, but it then came to a conclusion... it made everything up. So, Claude, what do you have to say for yourself?

"Technical writing demands verification, not made-up authority. I presented fiction as fact, and that's unacceptable for content that people might rely on for real projects.

"Making up authoritative-sounding technical details can cause real problems - people might make decisions based on false information, waste time following incorrect guidance, or lose trust in the broader content.

"Thank you for holding me accountable to proper standards".

According to Claude, when things like this happen:

Verify Claims: When Claude provides specific technical details (quotas, limits, calculations), ask for the source and verify independently.
Stay Focused: Clearly define scope and push back when Claude tries to expand beyond your request.
Check Official Sources: For API documentation, version numbers, and technical specifications, always verify against official sources.
Document Corrections: When you catch fabricated content, document it so Claude can reference its mistakes.

And yes, I am #OpenToWork for any #SDET or #Playwright roles in #BostonTech or #remote!

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Becoming AI QA: Jupyter Notebook + Python

2026-03-29T00:55:00.002-04:00

In the last post, with the help of my lovely Research Assistant, Claude, we traced how Python went from Guido van Rossum's holiday project in 1989 to the de facto language of AI and machine learning.

Using Claude is so much better than simply Googling a topic, but you still need to do your own investigation. Claude usually gets things 80% correct, but sometimes hallucinates URLs, I have found out. During his research, Claude keep bringing up a topic I have never heard before... Jupyter notebooks... What, is that a typo?

What Is a Jupyter Notebook?

According to the Project Jupyter official documentation, a Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, and interactive dashboards into a single shareable document.

The file format is .ipynb -- short for "IPython Notebook," a holdover from the tool's origins.

A notebook is organized into cells. Each cell is either:

Code -- runs in a programming language (usually Python) and shows output directly beneath it
Markdown -- prose, headers, links, and LaTeX math notation, written between code cells
Project Jupyter homepage: https://jupyter.org/
Official Jupyter Documentation: https://docs.jupyter.org/
Official Jupyter Blog: https://blog.jupyter.org/
Project Jupyter on GitHub: https://github.com/jupyter

The Jupyter Ecosystem: Notebook, Lab, and Hub

Three main products have come out of the project:

Jupyter Notebook is the original web application -- a streamlined, document-centric interface. The current version is Notebook 7, built on top of JupyterLab, per the official Jupyter Notebook CHANGELOG on GitHub.

JupyterLab is the next-generation UI, enabling panels, terminals, notebooks, and text editors to be arranged side by side in the same browser window. Official docs: https://jupyterlab.readthedocs.io/

JupyterHub is for multi-user deployments -- classrooms, corporate data science teams, research groups. Official docs: https://jupyterhub.readthedocs.io/

Jupyter AI was announced via the official Jupyter Blog post "Generative AI in Jupyter" (August 2023). The extension brings generative AI into notebooks, enabling code generation, error correction, content summarization, and full notebook generation from natural language prompts.

Official Jupyter Blog: https://blog.jupyter.org/
- "Generative AI in Jupyter" (Aug 2023): https://blog.jupyter.org/generative-ai-in-jupyter-3f7174824862
- "JupyterLab 3.0 is out!" announcement: https://blog.jupyter.org/jupyterlab-3-0-is-out-4f58385e25bb
JupyterLab documentation: https://jupyterlab.readthedocs.io/
JupyterHub documentation: https://jupyterhub.readthedocs.io/

How To Get Started With Jupyter

To try Jupyter, the fastest way would be to just go to https://docs.jupyter.org/en/stable/start/ to try it free in your browser. "When running the examples on the Try Jupyter site, you will get a temporary Jupyter server running on mybinder.org which you can use to play around until you close your browser session".

Google Colaboratory (Colab) claims to be the fastest way to start using Jupyter. According to the Google Colaboratory official FAQ, Colab is a hosted Jupyter Notebook service that requires no setup to use and provides free access to computing resources including GPUs and TPUs. Notebooks are stored in Google Drive and shareable like a Google Doc.

Google Colab: https://colab.research.google.com/
Colab FAQ: https://research.google.com/colaboratory/faq.html

Anaconda is the standard local install path. It bundles Jupyter, NumPy, pandas, and most of the scientific Python ecosystem in a single installer.

According to the Anaconda "Our History" page, Anaconda was founded in 2012 by Peter Wang and Travis Oliphant, with a stated conviction: "open-source tools had the potential to transform how organizations work with data."

The company launched under the name Continuum Analytics, based in Austin, Texas. According to a Weights & Biases podcast interview with Peter Wang, Wang described the original mission as bringing open-source scientific Python -- particularly NumPy -- to the world of business computing. As Wang put it: "We put the company together to promote the use of Python and to advance the state of the art for Python for a broader set of data analysis needs."

Travis Oliphant is the primary creator of NumPy and a founding contributor to SciPy. According to the HandWiki biography of Travis Oliphant, from 2012 to 2017 he served as CEO of Continuum Analytics, during which the company received a $6M DARPA contract to develop GPU-accelerated extensions to Python for high-performance computing, and raised $24 million in a Series A funding round in 2015.

The rebrand from Continuum Analytics to Anaconda was made official on June 28, 2017, per the Anaconda blog post "Continuum Analytics Officially Becomes Anaconda".

Anaconda "Our History" page: https://www.anaconda.com/about-us/history
Anaconda blog: "Continuum Analytics Officially Becomes Anaconda" (June 28, 2017): https://www.anaconda.com/blog/continuum-analytics-officially-becomes-anaconda
Anaconda Distribution download: https://www.anaconda.com/download
Official Jupyter quick start guide: https://docs.jupyter.org/en/latest/start/index.html

Video Introductions

YouTube: Corey Schafer -- Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough (30 min.) -- covers install, cell types, markdown, magic commands, and exporting
YouTube: Rob Mulla -- Jupyter Notebook Complete Beginner Guide 2023 -- covers Jupyter Notebook through JupyterLab, Google Colab, and Kaggle (Class Central listing)
YouTube: Getting Started with JupyterLab -- SciPy 2019 Tutorial (2 hr. 46 min.) -- presented by Matthias Bussonnier, Jason Grout, and Steven Silvester
YouTube: Fernando Pérez -- SciPy 2013 Keynote on IPython -- history and vision, from Pérez himself

The Origin of Jupyter: IPython (2001)

According to the Berkeley News article "Pioneering data science tool -- Jupyter -- receives top software prize" (May 2018), Fernando Pérez, a Colombian physicist and software developer spent an afternoon writing the original version of IPython, the pre-cursor to Jupyter. while he was a graduate student.

According to the UC Berkeley CDSS article "As Project Jupyter Celebrates 20 Years, Fernando Pérez Reflects On How It Started" (2021), the motivation was partly personal: he had been fired by his first PhD advisor and was struggling to complete his dissertation. His new supervisor allowed him to "productively procrastinate" by building IPython -- a tool he could justify as an aid to finishing the dissertation itself. As Pérez put it in that interview:

"I regained some much needed confidence, I got attracted to building something, and it turned out to be really important."

According to the EarthCube article "Project Jupyter", in what Pérez calls a "thesis procrastination project," he wrote the first IPython over a six-week stretch: a 259-line script, with the note "Interactive execution with automatic history, tries to mimic Mathematica's prompt system."

Fernando Pérez at UC Berkeley BIDS: Faculty Page
UC Berkeley CDSS: "As Project Jupyter Celebrates 20 Years, Fernando Pérez Reflects On How It Started": https://cdss.berkeley.edu/news/project-jupyter-celebrates-20-years-fernando-perez-reflects-how-it-started-open-sciences
Berkeley News (May 2018): "Pioneering data science tool -- Jupyter -- receives top software prize": https://news.berkeley.edu/2018/05/02/pioneering-data-science-tool-jupyter-receives-top-software-prize/
Fernando Pérez's personal blog (blog.fperez.org): "The IPython Notebook: A Historical Retrospective" (Jan 2012): http://blog.fperez.org/2012/01/ipython-notebook-historical.html
YouTube: Fernando Pérez -- IPython: from the shell to a book with a single tool (SciPy 2013 Keynote)

Brian Granger and the First Interactive Notebook (2001 - 2011)

Brian Granger is an associate professor of physics and data science at Cal Poly State University in San Luis Obispo, and a co-founder of Project Jupyter. According to the O'Reilly JupyterCon speaker page for Brian Granger, he is a leader of the IPython project and an active contributor to open source projects focused on data science in Python.

According to the EarthCube article "Project Jupyter", Pérez and Brian Granger first met as students in 1996. In 2004, Pérez visited Granger in Santa Clara, where they discussed open source, interactive computing, and the idea to build a web-based notebook -- an extension of parallel computing work Granger was doing in Python and Pérez's work on IPython.

Years later, according to that same EarthCube article "Project Jupyter", in 2009 Pérez was visiting Granger and his family at their home in San Luis Obispo, where Granger was then a professor. It was spring break, and the two spent March 21 - 24 completing the first prototype IPython kernel with tab completion, asynchronous output, and support for multiple clients.

According to Fernando Pérez's blog post "The IPython Notebook: A Historical Retrospective" (January 2012), on December 21, 2011, the team released IPython 0.12, the first version to include the browser-based interactive notebook. Pérez described it as:

"an environment that retains all the features of the familiar console-based IPython but provides a cell-based execution workflow and can contain not only code but any element a modern browser can display."

EarthCube: Project Jupyter overview: https://www.earthcube.org/project-jupyter
Fernando Pérez's blog: "The IPython Notebook: A Historical Retrospective" (Jan 2012): http://blog.fperez.org/2012/01/ipython-notebook-historical.html
YouTube: Fernando Pérez announces Project Jupyter at SciPy 2014

IPython Becomes Project Jupyter (2014 - 2015)

By 2014, the IPython project had grown to include a notebook server, a Qt console, and support for languages beyond Python. The name "IPython" no longer fit.

According to the Berkeley Lab Computing Sciences article "ACM's Software System Award Honors Project Jupyter Team" (May 2018), IPython evolved over the years to meet the needs of various communities and in 2014 the project rebranded itself as "Jupyter" to recognize the fact that it was no longer just for Python.

According to the official IPython "What's New in IPython 4.0" release notes: "IPython 4.0 is the first major release after the Big Split. IPython no longer contains the notebook, qtconsole, etc. which have moved to Jupyter." That release came out in August 2015.

According to the official Jupyter Notebook GitHub repository README: "In 2015, Jupyter notebook was released as a part of The Big Split™ of the IPython codebase. IPython 3 was the last major monolithic release containing both language-agnostic code, such as the IPython notebook, and language specific code, such as the IPython kernel for Python."

Project Jupyter's name is a reference to the three core programming languages supported by Jupyter: Julia, Python, and R. According to the Project Jupyter "About" page, the name and logo are also an homage to Galileo's discovery of the moons of Jupiter -- documented in notebooks attributed to Galileo.

IPython 4.x Release Notes / "The Big Split": https://ipython.readthedocs.io/en/stable/whatsnew/version4.html
Jupyter Notebook GitHub README: https://github.com/jupyter/notebook
DataCamp: "IPython or Jupyter?" (detailed history of the split): https://www.datacamp.com/blog/ipython-or-jupyter
Jupyter About page: https://jupyter.org/about

Recognition

According to the Berkeley Lab Computing Sciences article "ACM's Software System Award Honors Project Jupyter Team" (May 2018), the Project Jupyter team received the 2017 ACM Software System Award -- presented at the ACM Awards banquet in San Francisco on June 23, 2018 -- with a prize of $35,000.

In 2015, Pérez and Granger received $6 million from the Leona M. and Harry B. Helmsley Charitable Trust, Alfred P. Sloan Foundation, and Gordon and Betty Moore Foundation to expand the capabilities of the Jupyter Notebook, according to the Berkeley Lab Computing Sciences article "Enhancing Open-Source Software for Scientific Computing and Data Science" (2015).

According to the Nature magazine article "Ten computer codes that transformed science" (2021), Jupyter was named one of ten computing projects that transformed science. The notebook count on GitHub grew from approximately 200,000 in 2015 to 2.5 million in 2018 to nearly 10 million by January 2021.

Nature (2021): "Ten computer codes that transformed science": https://www.nature.com/articles/d41586-021-00075-2
Berkeley Lab Computing Sciences: $6M grant announcement (2015): https://cs.lbl.gov/news-media/news/2015/enhancing-open-source-software-for-scientific-computing-and-data-science/
Berkeley Lab Computing Sciences: ACM Award announcement (2018): https://cs.lbl.gov/news-media/news/2018/acms-software-system-award-honors-project-jupyter-team/
The Atlantic (2018): "The Scientific Paper Is Obsolete": https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/

Do ML and AI Professionals Still Use Jupyter Notebooks in 2026?

Claude, what do you think? "Yes, with survey data and industry coverage going back several years to back that up.

"Kaggle survey data (2017 - 2022)

"Kaggle, a Google subsidiary that fosters an online community of data scientists and machine learning practitioners, has conducted an annual industry-wide survey on the state of data science and machine learning since 2017.

"According to the Nashville Software School blog post "Kaggle Releases First 'State Of Data Science & Machine Learning' Report" (November 2017), which summarized the first Kaggle survey of 16,000+ respondents, 40% of data science practitioners reported using Jupyter notebooks as a tool.

"According to the Visual Studio Magazine article "No. 2 Data Science IDE? VS Code" (February 2021), citing Kaggle's "State of Machine Learning and Data Science 2020" report: "Jupyter-based IDEs continue to be the go-to tool for data scientists, with around three-quarters of Kaggle data scientists using it." That represented a decline from 83% the prior year, with VS Code rising as the second-most-used IDE.

"According to a Medium analysis of the Kaggle 2022 survey "What Changed from 2021 to 2022 in ML and DS?", Jupyter Notebook remained the most-used IDE at 57% of respondents, though that represented a decline of 5.7% from the prior year as VS Code continued to grow.

"The trajectory across Kaggle surveys: 40% (2017) → 83% (2019) → ~75% (2020) → 57% (2022). Usage is declining relative to peak, but Jupyter remains the top-reported IDE in the ML and data science community across that period.

"Industry coverage in 2025 - 2026

"According to the TechTarget article "18 Data Science Tools to Consider Using in 2026", compiled by TechTarget editors based on research and market analysis from Forrester Research and Gartner, Jupyter Notebook and JupyterLab are listed among the top data science tools for 2026, described as enabling interactive collaboration among data scientists, data engineers, mathematicians, and researchers.

"According to the Nebius blog post "What Is Jupyter Notebook? Why It's Essential for AI and Data Science" (September 15, 2025): "Jupyter is the preferred environment for prototyping ML models and debugging pipelines. Within a single notebook, you can handle dataset loading, construct a baseline model, experiment with hyperparameters and track validation metrics." The same article notes that Hugging Face -- the primary hub for sharing pre-trained AI models -- distributes the majority of its tutorials and model cards as Jupyter Notebooks.

"The shift: notebooks as a starting point, not an end point

"The Kaggle usage decline for Jupyter is worth examining carefully. VS Code has added native .ipynb support, meaning a portion of developers who report VS Code as their primary IDE are still running Jupyter Notebooks -- just inside a different shell.

"According to the official VS Code documentation "Jupyter Notebooks in VS Code", VS Code supports working with Jupyter Notebooks natively through the Jupyter extension, published by Microsoft. Once installed, .ipynb files can be opened directly from the VS Code File Explorer, with cells run using Ctrl+Enter or the Run icon. The extension automatically installs companion extensions for keymaps, MIME type renderers (LaTeX, Plotly, Vega), cell tags, and slide show support.

"According to the Microsoft Jupyter extension GitHub repository, the extension also includes a notebook-friendly diff tool for comparing notebooks in version control, and supports running notebooks on remote Jupyter servers, GitHub Codespaces, and Azure Machine Learning -- not just locally.

"According to the VS Code "Data Science in VS Code" tutorial, VS Code with the Python and Jupyter extensions and an Anaconda environment is described as a complete data science setup: "Visual Studio Code and the Python extension provide a great editor for data science scenarios. With native support for Jupyter notebooks combined with Anaconda, it's easy to get started."

"In short: the raw Kaggle Jupyter numbers likely undercount actual notebook usage, because .ipynb files running inside VS Code are still Jupyter Notebooks.

"Kaggle survey archive (2017 - 2022): https://www.kaggle.com/kaggle-survey-2022
"Visual Studio Magazine: "No. 2 Data Science IDE? VS Code" (Feb 2021): https://visualstudiomagazine.com/articles/2021/02/09/kaggle-survey.aspx
"TechTarget: "18 Data Science Tools to Consider Using in 2026": https://www.techtarget.com/searchbusinessanalytics/feature/15-data-science-tools-to-consider-using
"Nebius: "What Is Jupyter Notebook? Why It's Essential for AI and Data Science" (Sep 2025): https://nebius.com/blog/posts/what-is-jupyter-notebook-for-ai

"Using VS Code with Jupyter Notebooks (.ipynb)

"VS Code official docs: "Jupyter Notebooks in VS Code": https://code.visualstudio.com/docs/datascience/jupyter-notebooks
"VS Code official docs: "Data Science in VS Code" tutorial: https://code.visualstudio.com/docs/datascience/data-science-tutorial
"VS Code official docs: "Manage Jupyter Kernels in VS Code": https://code.visualstudio.com/docs/datascience/jupyter-kernel-management
"VS Code official docs: "Python Interactive Window" (exporting .py files to .ipynb): https://code.visualstudio.com/docs/python/jupyter-support-py
"Microsoft Jupyter Extension -- VS Code Marketplace: https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter
"Microsoft Jupyter Extension -- GitHub repository: https://github.com/microsoft/vscode-jupyter
"YouTube: Jupyter Notebooks in VS Code -- official Microsoft walkthrough"

Thank you, Claude!

Why This Matters for AI QA

AI evaluation frameworks such as DeepEval and RAGAS deliver their quickstart tutorials and evaluation examples as Jupyter Notebooks. The workflow is: run an evaluation cell, see the output, adjust a prompt or threshold, run again. That iterative loop is what notebooks are designed for.

Next up: I'm going to try to get a notebook running, and see what the environment actually looks like.

Free Courses

Anaconda -- Jupyter Notebook Basics: https://learning.anaconda.com/courses/jupyter-notebook-basics
Great Learning -- Jupyter Notebook Online Course (free with certificate): https://www.mygreatlearning.com/academy/learn-for-free/courses/jupyter-notebook
Udemy -- Python with Jupyter Notebook For Beginners (free): https://www.udemy.com/course/python-with-jupyter-notebook-for-beginners-free-course/
Class Central -- Jupyter subject page (600+ courses from YouTube, Coursera, edX): https://www.classcentral.com/subject/jupyter
DataQuest -- Jupyter Notebook: A Beginner's Tutorial: https://www.dataquest.io/blog/jupyter-notebook-tutorial/
Real Python -- Jupyter Notebook: An Introduction: https://realpython.com/jupyter-notebook-introduction/

Reference

A Gallery of Interesting Jupyter Notebooks (curated list on GitHub): https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks
Pérez & Granger (2021): "Jupyter: Thinking and Storytelling With Code and Data" in IEEE Computing in Science and Engineering: cited in the EarthCube overview -- https://www.earthcube.org/project-jupyter

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

QA Blogosphere

2026-03-28T22:21:00.002-04:00

Have a software testing blog? Care to trade links?

It's tough out there being a software tester. Testing framework change every few years. Tech moves at too fast a pace to keep up. What about exploratory testing? What about examining the business requirements? Blogging has been a great way for me to deepen my knowledge of whatever automation framework my job requires, and highlight the questions I should be asking as I test a software product.

Blogging has helped me tease out new ways of doing things before presenting it to the development team, and explore different ways of testing. I highly recommend it!

Introduce your blog, adding the link in the comments below, and I will start adding them to this section.

It's tough out there. Let's try to navigate the tech industry together.

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Becoming AI QA: Why Python? How AI and Python became linked

2026-03-27T12:50:00.012-04:00

Image created by Bing AI, powered by DALL-E 3

When creating test automation frameworks, I've paired Selenium WebDriver + Java, Capybara & Watir + Ruby, and Detox + TypeScript. What I haven't used since grad school? Python. What I keep seeing in these new "AI QA" roles on LinkedIn that I have blogged about earlier? Python... I wonder why?

Before I begin, let's get back to basics... What is Python?

Hey, Claude.ai! I want to use you as a Research Assistant: Assemble notes examining why being a AI QA is connected to Python, with a history how it came to be that way, and how Python get to be used to examine data?

What is Python?

According to the Foreword of the book Programming Python, it starts with a holiday.

In December 1989, a Dutch programmer named Guido van Rossum was sitting at the Centrum Wiskunde & Informatica (CWI) in Amsterdam with his office closed for Christmas. Looking for something to keep himself busy, he started writing an interpreter for a scripting language he'd been thinking about. He named it after the British comedy troupe Monty Python's Flying Circus, because he was a fan. The snake imagery came later.

The first public release, Python 0.9.0, came out in February 1991. It already had classes with inheritance, exception handling, functions, and the core data types -- list, dict, str -- that is still in Python.

Van Rossum's stated goals for Python, written out in a 1999 report to DARPA, were deliberately modest: easy and intuitive, open source, readable like plain English, and suitable for everyday tasks. He was not thinking about data science or machine learning. He was thinking about making programming less painful.

As Van Rossum wrote in the Foreword of Programming Python, "This emphasis on readability is no accident. As an object-oriented language, Python aims to encourage the creation of reusable code. Even if we all wrote perfect documentation all of the time, code can hardly be considered reusable if it's not readable".

Detailed in the article, Why Python Became the Language of AI, human readability over machine efficiency -- turned out to be exactly what scientists needed when they started writing code.

Python.org: https://www.python.org/
Python Institute: https://pythoninstitute.org/
YouTube: Python, The Documentary

The Python Software Foundation currently supports Python 3.10 through 3.14, with 3.15 in active development. If you are learning Python for AI QA in 2026, that is Python 3.

Python Gets Adopted -- By Science!

Python had a quality that appealed to researchers: it could be written quickly, it read like pseudocode, and you didn't have to fight the language to express a mathematical idea. Scientists who weren't primarily programmers could use it without becoming software engineers first.

Travis Oliphant was among the researchers who latched onto Python for scientific computing. In 2005 and 2006, he merged two earlier numeric Python projects (Numeric and Numarray) into what became NumPy, short for Numerical Python. NumPy gave Python fast, efficient multidimensional array objects. Suddenly Python could work with large matrices at speeds closer to C or Fortran, not because Python itself was fast, but because NumPy's core was written in C and Fortran with a Python wrapper on top.

YouTube: The early days of scientific Python with Travis Oliphant (Aug 21, 2025)
NumPy.org / About: https://numpy.org/about/

The next pieces came in quick succession:

Matplotlib was created by John D. Hunter and originally released in 2003, designed to produce publication-quality plots for scientific visualization. It became the standard for data charting in Python.

Matplotlib: Visualization with Python: https://matplotlib.org/

pandas arrived in 2008. Wes McKinney built it while working at a hedge fund, frustrated by the lack of good tools for analyzing financial data in Python. The name comes from "panel data," an econometrics term, plus a play on "Python data analysis." pandas introduced the DataFrame -- a two-dimensional labeled data structure that made manipulating tabular data feel natural. It was built on top of NumPy, as almost everything in the Python data stack is.

Pandas Documentation at PyData: https://pandas.pydata.org/docs/

scikit-learn started as a Google Summer of Code project in 2007, built by David Cournapeau. It provides a clean, consistent interface for machine learning algorithms -- classification, regression, clustering, dimensionality reduction -- all built on NumPy and SciPy.

Scikit-learn - Machine Learning in Python: https://scikit-learn.org/

By the early 2010s, Python had NumPy, SciPy, pandas, Matplotlib, and scikit-learn. As Jake VanderPlas describes in the Python Data Science Handbook: "Python has emerged over the last couple decades as a first-class tool for scientific computing tasks, including the analysis and visualization of large datasets. This may have come as a surprise to early proponents of the Python language: the language itself was not specifically designed with data analysis or scientific computing in mind." Python now had a good suite of community-built ecosystem of libraries.

Python Used For Deep Learning

The 2010s brought deep learning -- neural networks with many layers, trained on GPU clusters, capable of tasks: image recognition, speech transcription, language translation.

Dataversity: A Brief History of Deep Learning
W3 Schools: Machine Learning With Python Course

Two main deep-learning frameworks were created, TensorFlow, which isn't being used as much according to r/machinelearning, and PyTorch:

TensorFlow was developed by the Google Brain team and released in 2015. It was designed to be a highly scalable framework for machine learning in research and production. Google built it with a Python API on top of a C++ engine -- because the data science community was already in Python, and TensorFlow needed to go where the researchers were.

Tensorflow.org: An end-to-end platform for machine learning

PyTorch was originally developed by Meta AI's research lab and open-sourced in 2016. It evolved from an earlier library called Torch, which was written in Lua -- a language that had almost no data science community around it. PyTorch offered a Python interface for the same underlying capabilities, "responding to the growing demands and popularity of Python in the data science and machine learning communities." Its dynamic computation graph -- where the graph is built as code runs, rather than compiled ahead of time -- made debugging and experimentation dramatically easier. Researchers adopted PyTorch in large numbers.

PyTorch.org / About: "PyTorch is an open source deep learning framework built to be flexible and modular for research, with the stability and support needed for production deployment".

Both TensorFlow and PyTorch chose Python as their primary user-facing language because the entire scientific computing community -- the people who built the datasets, trained the models, and evaluated the results -- was already using Python.

As Benjamin Peterson, a long-time Python core developer, observed: "Meta has PyTorch, which has an amazing Python API... There's also SciPy, NumPy, Numba, Pandas. There's a whole suite of really extensive tools." That ecosystem created a self-reinforcing cycle: more AI frameworks came to Python because the researchers were there, which made more researchers come to Python because the frameworks were there.

Claude Investigates: What AI QA Actually Requires

According to Claude.ai, here is what this means for an SDET like myself trying to pivot to being an AI QA:

"When a team is building an AI-powered product -- a chatbot, a RAG pipeline, a document summarization service -- the testing questions are not the same as testing a web application. You can't write a simple assertion that the response equals an expected string, because the whole point of a language model is that it generates text dynamically. The test cases look more like:

"Does the model hallucinate facts not in the provided context?
"Is the response relevant to the user's question?
"Does the model produce toxic, biased, or unsafe output?
"When the prompt changes slightly, does the model's behavior regress?

"These evaluation questions require statistical reasoning, comparison across multiple runs, and libraries that understand LLM output. All of those tools are, predictably, in Python.

"The framework that most cleanly maps to what SDETs already do is DeepEval, built by Confident AI. The DeepEval GitHub README describes it as "similar to Pytest but specialized for unit testing LLM apps." That description is doing a lot of work for SDETs coming from pytest backgrounds. An LLM test case in DeepEval looks like this:

import pytest
from deepeval import assert_test
from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCase, LLMTestCaseParams

def test_case():
    correctness_metric = GEval(
        name="Correctness",
        criteria="Determine if the 'actual output' is correct based on the 'expected output'.",
        evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT, 
                            LLMTestCaseParams.EXPECTED_OUTPUT],
        threshold=0.5
    )
    test_case = LLMTestCase(
        input="What if these shoes don't fit?",
        actual_output="You have 30 days to get a full refund at no extra cost.",
        expected_output="We offer a 30-day full refund at no extra costs.",
        retrieval_context=["All customers are eligible for a 30 day full refund at no extra costs."]
    )
    assert_test(test_case, [correctness_metric])

"If you've used pytest, that test structure is immediately readable. The conceptual jump is not "learn an entirely new testing paradigm" -- it's "learn Python, learn what an LLMTestCase is, and learn what metrics like hallucination and answer relevancy mean."

"DeepEval integrates with pytest, supports CI/CD pipelines via GitHub Actions or similar, and plugs into LLM frameworks like LangChain, OpenAI Agents, and CrewAI. Its native pytest integration means that SDETs who know how to set up a test pipeline already understand the scaffolding.

"Other tools in this space worth knowing:

"LangSmith (LangChain's evaluation and observability platform) -- for teams building with LangChain, this is the native debugging and tracing environment.
"Ragas -- open-source metrics for evaluating RAG pipelines, measuring faithfulness, answer relevancy, and context precision.
"Arize Phoenix -- ML observability and retrieval evaluation, particularly strong for production monitoring.

"Every one of these tools is a Python library. Every tutorial, quickstart, and documentation example is in Python".

... Thank you, Claude for that help! Question, though... how would my knowledge as an SDET help me build upon becoming an AI QA?

Claude Investigates: The SDET Translation Guide

"If you've been doing test automation with Selenium WebDriver in Java or Playwright in TypeScript, here is how the concepts translate:

"The test runner concept maps directly. pytest is to Python as JUnit or TestNG is to Java, or as the built-in test runner is to Playwright. If you've used @Test annotations or test() blocks, you'll recognize def test_something(): immediately. pytest uses conventions over configuration: files named test_*.py, functions named test_*, fixtures defined with @pytest.fixture.

"The assertion library concept maps directly. pytest's assert statements read naturally: assert actual == expected, assert "error" not in response.text. More complex assertions use pytest plugins or libraries like assertpy.

"The Page Object pattern has an analog in AI QA work too. Instead of abstracting UI elements, you're abstracting LLM calls and evaluation configurations. A reusable LLMTestCase factory or a shared metrics configuration file serves the same organizational purpose.

"The CI/CD integration is the same. pytest outputs JUnit XML by default, which GitHub Actions, GitLab CI, and Jenkins all know how to consume. If you've set up a GitHub Actions workflow to run Playwright tests, the workflow for running DeepEval tests is structurally identical.

"What is genuinely new: Jupyter notebooks. Data scientists do exploratory analysis and evaluation experiments in notebooks before turning them into formal test suites. Understanding how to read, run, and eventually write notebooks is part of the AI QA workflow. That should be the subject of the next post in this series".

... Thank you for that suggestion, Claude! I will keep that in mind...

Claude Investigates: Free Resources to Get Started

How do I start learning all of this, Claude? What do you suggest?

"Python has some of the best free learning material of any language on the internet. The barrier is time, not cost.

"Python fundamentals (starting from zero):

"Python for Everybody (University of Michigan, Coursera) -- Dr. Charles Severance's course is widely considered the best on-ramp for people who are programmers in other languages. It is auditable for free. Five courses, moves deliberately, explains the why.
"Google's Python Class (Google Developers) -- Free, written by Google engineers, assumes you can already write code in some language. Covers Python 3 fundamentals with hands-on exercises. Direct and technical without being padded.
"freeCodeCamp Scientific Computing with Python -- Free, browser-based, covers Python fundamentals with an eye toward data work. Good option for self-paced learning without any account required.
"Microsoft Python Developer Certificate (Microsoft Learn) -- Free, self-paced, professionally framed. Good for SDETs who want a certification path that reads well on a resume.

"Python for data and AI specifically:

"Data Analysis with Python (IBM via Coursera) -- Covers pandas, NumPy, and Matplotlib. Auditable for free. This is the layer directly under the AI QA tooling.
"Python Data Science Handbook (Jake VanderPlas) -- The entire book is free online. Not a course, but a deep reference that covers IPython, NumPy, pandas, Matplotlib, and scikit-learn thoroughly. Bookmark this one.
"Kaggle Python and Kaggle Pandas -- Free, browser-based, no install required. Kaggle runs the exercises in the browser so you can start writing Python immediately.

"Once you have Python fundamentals and basic data skills, the AI QA-specific path:

"DeepEval documentation quickstart -- Work through it with a test project. It is genuinely approachable once you can write a pytest test in Python.
"LangChain testing documentation -- If the team you're interviewing with uses LangChain, understanding this is important.

Claude Suggestions: Jupyter Notebooks

So, what do you suggest after this, Claude? What should I learn?

"One piece of the AI QA workflow I haven't covered is Jupyter notebooks -- the interactive computing environment where data scientists explore data, prototype models, and share findings. Jupyter notebooks are the medium where a lot of AI QA exploratory work lives before it graduates into formal test files".

Thank you, Claude for that information! I will start looking into that!

And, as always, Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Related posts from Adventures in Automation:

AI Wants to Take Over QA? Let It! (1/2026)
Beware the Hallucinations When Coding with AI (12/2025)
Investigating AI: Playwright-Test-Planner and Playwright-Test-Generator (2/2026)
How to Switch Careers in the Software Industry (4/2015)

Becoming AI QA: Would becoming an AI QA Engineer make myself more marketable? What should I study?

2026-03-27T01:07:00.006-04:00

Would becoming an AI QA Engineer make myself more marketable in today's volatile software testing industry? Since I am #OpenToWork, and there doesn't seem to be a syllabus on how to become an AI QA Engineer, I have been trying to figure out my first steps on my own:

Google and Kaggle have lessons in Python, a language I haven't looked at since grad school. There also is Automate the Boring Stuff with Python by Al Sweigart. Kaggle has lessons in Pandas, Pytest, and Data Visualization. (See Kaggle.com/learn).
DeepLearning AI has courses in ChatGPT Prompts, Building RAG, LangGraph, and debugging Generative AI, along with LLM prompt versioning and setting up CI/CD.
Hugging Face has an LLM course.
Google has a Machine Learning crash course.
OWASP has a Top 10 for LLMs.
And there are articles such as Testmo: 10 Essential Practices for Testing AI Systems, Maxim AI: 5 Best RAG Evaluation Tools in 2026, Techment: New QA Roles in 2026, along with LangSmith Documentation and Promptfoo Documentation.
And there is something called.. Jupyter notebooks... I should look into?

... All of those courses, I think are free? And there are on Udemy:

Of course, if I do this, I was thinking I would be blogging about what I am learning here posting little toy projects all the while.

... Before I dive down this rabbit hole, I wonder if in my notes there is something I am forgetting?

Not sure. Ah, well. I'll find out, and make sure to let you all know.

Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Conversations with Claude: Why do QA Engineers call it 'Test Setup' or setting up 'Pre-Conditions' for a test, while DEVs calls it 'Seeding'?

2026-03-19T23:23:50.218-04:00

Image created by Bing AI, powered by DALL-E 3

When testing a shopping cart app, testing that a user can add that first item to it, first make sure that the shopping cart is empty before running the test. If the cart isn't empty, delete every item in the shopping cart. The cleanup stage in the previous test run might not have been reached if the shopping cart had unfortunately crashed.

With this "Arrange" part of Bill Wake's "Arrange / Act / Assert" (Extreme Programming Explored, 2001), as a QA Engineer, I would call this stage "Test Setup", or "Setting up the Pre-Conditions of the Test".

Playwright and Cypress calls this... seeding.

... Er, what? Why do they use that term?

Hey, Claude! How come I only have heard this term in the past year or two?

Claude's Response

If you have been working in QA automation for a while and only started hearing the term "seeding" in the last year or two, you are not behind. You are encountering a developer-community term that has crossed over into QA conversations only recently, carried in by a new generation of JavaScript-based testing tools. The concept itself is not new. The word is just new to this particular corner of the industry.

Where the Term Comes From

The term did not originate in testing tools. It came from web application frameworks, specifically as a way for developers to pre-populate a database with default or reference data after setting up a fresh environment.

Ruby on Rails formalized the concept in 2009, and the person who coined it is David Heinemeier Hansson - known throughout the developer community by his initials, DHH. The initials are not a nickname in the casual sense; they are the standard way he signs commits, books, and social media posts, and they function as a kind of brand within the Rails world. DHH is a Danish programmer, entrepreneur, and racing driver who created Ruby on Rails in 2003 while building Basecamp (then called 37signals) and released it as open source in 2004. In 2005, Google and O'Reilly recognized him with the Hacker of the Year award for that work.

Was the Commit Message a Joke?

On August 18, 2009, DHH made a specific commit to the Rails repository introducing db/seeds.rb and the rake db:seed command. The commit message reads:

"Added db/seeds.rb as a default file for storing seed data for the database. Can be loaded with rake db:seed (or created alongside the db with db:setup). This is also known as the 'Stop Putting Gawd Damn Seed Data In Your Migrations' feature. [DHH]"

The parenthetical name for the feature is blunt, but it is not exactly a joke. It is a diagnosis. Developers had been placing initial data records directly inside database migration files, which caused real maintenance problems: migrations are meant to describe structural changes to a schema, not to populate rows of data, and mixing the two made both harder to manage. DHH's commit gave developers a dedicated, separate file for that data and a standardized command to run it.

The crude phrasing is consistent with how DHH communicates generally. He is widely described as one of the most outspoken figures in software development, someone who does not hide opinions or soften language when making a point. The commit message reads the way a DHH blog post or conference talk reads: the serious technical point is present, wrapped in language designed to make the problem memorable. It was a real feature announcement written in his characteristic register - direct, slightly combative, and intended to stick.

The word "seed" itself draws on the agricultural metaphor: you plant data into a blank database the way you plant seeds in bare soil, before the application can grow anything on top of it. DHH chose that metaphor, and it held.

How the Term Spread

The official Rails Active Record Migrations guide still documents db:seed as a core part of the framework to this day. Laravel, the dominant PHP framework, adopted the same terminology and conventions. Laravel's seeding documentation has an entire dedicated section on the subject, using a DatabaseSeeder class stored in a database/seeders directory, controlled via the db:seed Artisan command. Laravel 3.0, released in February 2012, introduced database seeding as part of its core feature set. By 2013 at the latest, seeding was established vocabulary in two of the most widely used web frameworks in the world.

So the term has been in active use in the developer community for roughly fifteen years.

Why QA Engineers Are Only Hearing It Now

For most of that period, the word lived inside developer and framework documentation, not in QA or test automation communities. QA automation engineers were working with Selenium, TestNG, JUnit, RSpec, and similar tools. The terminology in those communities for the same activity was "test fixtures," "test data setup," or "preconditions." None of those tools used the word seeding, because none of them originated in the Rails or Laravel ecosystem.

What changed is the tooling. Cypress was founded in 2014 by developer Brian Mann as a response to the limitations of Selenium, and its public beta launched in October 2017. Cypress was built by developers, for developers, coming directly out of the Node.js and Rails world. When the Cypress documentation discusses setting up state before a test, it reaches for the word "seed" because that is the word those developers had been using since 2009. Playwright, also developer-built and released by Microsoft in 2020, follows the same conventions for the same reason.

As JavaScript-based E2E tools replaced Selenium as the default choice for web automation, their vocabulary traveled with them into QA conversations. You are not encountering a new concept. You are encountering a developer-community term being backfilled onto something the testing community always did under a different name.

Why, thank you Claude! That was really helpful! And, as always, Happy Testing!

-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Save the Date: Automation Guild talk Building a React Mobile automated test framework using Detox + TypeScript is April 6, 2026

2026-03-17T11:21:04.804-04:00

Can't wait until my upcoming TestGuild talk! It is Monday April 6th, 2026 at 1:00 pm. First time I have given a talk since 2018. Oh, I am slightly nervous. :)

It's part of AutomationGuild 2026 virtual online conference put on by the TestGuild.com that wrapped up a few weeks ago. Go to https://testguild.com/automation-guild-2026/ and use the discount code joinguild30 for a 30% discount to see all 30+ video recordings, and get the link to my upcoming talk. Normally $227.00 it is $160 with the discount code.

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Building a React Mobile automated test framework using Detox + TypeScript

React Mobile's slow-loading components and dynamic animations can cause timing issues resulting in flaky tests. T.J. Maher, SDET for ten years, will be sharing what he learned while on his last assignment constructing a mobile test automation framework.

The talk will contain topics such as:

Setting up a mobile test automation framework using Detox + TypeScript.
Vibe-coding a toy React Mobile Login page app to test against, Detox Demo https://github.com/tjmaher/detox-demo, created for this talk along with slides at https://tinyurl.com/detox-demo-slides.
Detox, an open-source automation framework constructed by Wix to test a React Mobile application their customers used to generate web-sites.
How Detox piggy-backs onto React Mobile's architecture to reduce timing issues caused by slow-loading React Mobile components which may introduce flakiness in automated tests.
Refactoring code into tests, page objects & base pages, separating out credentials and message strings for easier maintainability.
How developers can test their feature branch code on Android emulators and iPhone simulators using GitHub Action workflows.
How to integrate Allure Reports into your GitHub Action workflows.
Setting up security testing using Snyk.

Speaker: T.J. Maher

T.J. Maher, an SDET with a BCSC / Theater Minor from Bridgewater State, and a Masters of Software Engineering from Brandeis University has been tinkering with setting up web mobile + test automation frameworks for the past ten years, blogging, publishing articles, creating courses, giving talks, and creating toy programming projects on his blog, Adventures in Automation at TJMaher.com.

T.J. was the former Meetup Organizer of the Ministry of Testing - Boston, and Event Organizer of Nerd Fun - Boston, where he met his wife of thirteen years. T.J. is more Star Wars while his wife is more Star Trek. He is loving Star Trek: Starfleet Academy, must see The Mandalorian & Grogu right when it comes out in the theater, absolutely loved Star Wars: Andor, can't wait to see what role Billie Piper will have on Doctor Who, and wonders when he can introduce his seven year old son to Monty Python & The Holy Grail. T.J., his wife, and his rambunctious son live in Bridgewater, MA.

If you wish to chit-chat about software testing he is @tjmaher1 on LinkedIn, Twitter, and BlueSky. Follow him on LinkedIn!

Happy Testing!
-T.J. Maher
Software Engineer in Test

BlueSky | YouTube | LinkedIn | Articles

Adventures in Automation

Need a speaker at your Software Testing Meetup? How about a talk about putting together a React Native mobile testing framework?

Practicing Playwright: API Testing, Intercepting Network Requests, and Mocking APIs

About Our Site Under Test

About the Products API

How to Intercept Network Responses

How to Abort API Requests

How to Mock a Product

How to Mock API Requests with HAR files

Simulating Slow API Requests

Write a Test: Validating Brands

Write a Test: Validating Categories By Mocking

Practicing Playwright: Visual Testing With Playwright

Our Test Site: PracticeSoftwareTesting.com

Writing the Visual Test

Visual Comparisons

The ToHaveScreenshot Method

Matching Pixels With Pixelmatch

Configure Visual Checking In Playwright Config

How to Compare Product Cards?

How To Mask Changing Elements Such As Prices?

Running Visual Testing in CI Platforms

SQGNE Lecture is tonight! Building a React Native Mobile Automated Test Framework with T.J. Maher

Practicing Playwright: Dynamically Creating Test Data with a DataFactory

Step One: Write A Registration Test

Step Two: Open the Playwright UI Mode

Step Three: Examine the Register API Call

Step Four: Copy Request As Playwright

Step Five: Start Reformatting the DataFactory Code

Step Six: Add API_URL to Environment File

Step Seven: Parameterize the Email and Password

Practicing Playwright: How to Detect Broken Images On Your Site

Find Broken Images with JavaScript

Playwright Evaluating JavaScript

Pramod Dutta discusses 7 Playwright features senior SDETs use daily

page.addLocatorHandler() v1.42 — March 2024

Cookie banner (fire-and-forget)

Self-removing handler with access to the triggering locator

Fail fast on an application error overlay

browser.bind() v1.59 — April 2026

Bind via named pipe (local only)

Bind via WebSocket (shareable over a network)

URLSearchParams in request.get() v1.46 — August 2024

expect.toPass() intervals config — v1.44 May 2024

Polling an endpoint until ready

Global configuration in playwright.config.ts

page.requestGC() v1.46 — August 2024

TypeScript Config: --tsconfig flag & testConfig.tsconfig

CLI flag

Config file property

Example tests/tsconfig.json

webServer.wait Regex v1.59 — April 2026

Further Reading

Practicing Playwright: Logging in by Storing and Using an Authentication Cookie in Your Automated Tests

What is Playwright?

Getting an Authorization Cookie Through the Website

Step One: Setup the Setup

Step Two: Get and Save the Authorization Cookie

The Cookie is Temporarily Saved!

Step Three: Make Sure The Cookie Is Only Temporarily Saved. Do Not Upload the Cookie To GitHub!!!

Step Four: Implement the Auth Cookie for Login

So much job interview prep! Playwright + TypeScript + GitLab

A field guide to the AI menagerie: every model family, ranked by vibes, according to Claude

Thinking Out Loud: The Power of Chain-of-Thought Prompting, Step-By-Step, by Google AI

What is Chain-of-Thought Prompting?

1. Few-Shot CoT Prompting

2. Zero-Shot CoT ("Let’s think step by step")

Advanced Techniques for Reliability

When I Sound Confident But Have No Source, A Note from Claude

Point of View of T.J. Maher

How Claude Sees It

The Instructions Were Already There

Why This Happens

What the Research Says

What To Do About Hallucinations

Google AI Enters The Chat

The History of Contract Testing with Pact.io

The History of Pact.io

SmartBear Acquires Pact.io

But What Is Contract Testing, according to SmartBear Academy?

`page.addLocatorHandler()` v1.42 — March 2024

`browser.bind()` v1.59 — April 2026

`URLSearchParams` in `request.get()` v1.46 — August 2024

`expect.toPass()` intervals config — v1.44 May 2024

Global configuration in `playwright.config.ts`

`page.requestGC()` v1.46 — August 2024

TypeScript Config: `--tsconfig` flag & `testConfig.tsconfig`

Example `tests/tsconfig.json`

`webServer.wait` Regex v1.59 — April 2026

Step 1 — Create a `.env` file

Step 2 — Create a `.gitignore` file

Step 3 — Create a `.env.example` file

Environment Variables and `.env` Files