Beat Fluri

Wer in der Softwareentwicklung von KI spricht, meint zumeist Code-Assistenten. Wenn es um die Verifikation und den Betrieb von Software und Systemen geht, erweist sich indes das bisher wenig beachtete AIOps als besonders wirkungsvoll.

Der Einfluss von KI wächst stetig – auch in der Softwareentwicklung. Wie KI die Entwicklungsprozesse verändert, verrät Beat Fluri, CTO von Adnovum. Interview: Marc Landis

Hochqualifizierte Softwareentwicklerinnen und -entwickler sind nach wie vor schwierig zu finden. Mit KI als Unterstützung können wir dem Mangel schrittweise entgegenwirken. Wie das heute schon funktioniert und ­warum diese Entwicklung begeistert.

Bevor ein Software-Gilde abgerissen und neu aufgebaut wird, sollte jeweils die Tragfähigkeit der vorhandenen Lösung beurteilt werden. Oft ist eine sanfte Renovation sinnvoller. Hier den richtigen Entscheid zu treffen, ist allerdings nicht trivial.

Source code comments are a valuable instrument to preserve design decisions and to communicate the intent of the code to programmers and maintainers. Nevertheless, commenting source code and keeping comments up-to-date is often neglected for reasons of time or programmers obliviousness. In this paper, we investigate the question whether developers comment their code and to what extent they add comments or adapt them when they evolve the code. We present an approach to associate comments with… Mehr anzeigen

Source code comments are a valuable instrument to preserve design decisions and to communicate the intent of the code to programmers and maintainers. Nevertheless, commenting source code and keeping comments up-to-date is often neglected for reasons of time or programmers obliviousness. In this paper, we investigate the question whether developers comment their code and to what extent they add comments or adapt them when they evolve the code. We present an approach to associate comments with source code entities to track their co-evolution over multiple versions. A set of heuristics are used to decide whether a comment is associated with its preceding or its succeeding source code entity. We analyzed the co-evolution of code and comments in eight different open source and closed source software systems. We found with statistical significance that (1) the relative amount of comments and source code grows at about the same rate; (2) the type of a source code entity, such as a method declaration or an if-statement, has a significant influence on whether or not it gets commented; (3) in six out of the eight systems, code and comments co-evolve in 90% of the cases; and (4) surprisingly, API changes and comments do not co-evolve but they are re-documented in a later revision. As a result, our approach enables a quantitative assessment of the commenting process in a software system. We can, therefore, leverage the results to provide feedback during development to increase the awareness of when to add comments or when to adapt comments because of source code changes. Weniger anzeigen

Changes are the heartbeat of a software system. Software must change to reflect new business, pro­cess, and technology requirements, or it will become progressively less useful. Software typically grows and becomes more complex, inducing more time and effort for performing changes. Software archives such as source code version-control systems and issue-tracking systems (for bugs and change requests) are rich sources to examine what changes have what impact on the software. A software evolution… Mehr anzeigen

Changes are the heartbeat of a software system. Software must change to reflect new business, pro­cess, and technology requirements, or it will become progressively less useful. Software typically grows and becomes more complex, inducing more time and effort for performing changes. Software archives such as source code version-control systems and issue-tracking systems (for bugs and change requests) are rich sources to examine what changes have what impact on the software. A software evolution analysis platform called Evolizer analyzes change histories and potential support for evolution. Change types, a core part of the analysis, help discover significant changes and change patterns. A tool called ChangeDistiller enables fine-grained change type extraction and analysis to reason about coding conventions, control or exception flow, and even code and comment coevolution. An investigation of open source and commercial software systems contributed to a deeper understanding of how researchers can actively support software evolution in an integrated development environment. Weniger anzeigen

A key issue in software evolution analysis is the identification of particular changes that occur across several versions of a program. We present change distilling, a tree differencing algorithm for fine-grained source code change extraction. For that, we have improved the existing algorithm of Chawathe et al. for extracting changes in hierarchically structured data. Our algorithm detects changes by finding a match between nodes of the compared two abstract syntax trees and a minimum edit… Mehr anzeigen

A key issue in software evolution analysis is the identification of particular changes that occur across several versions of a program. We present change distilling, a tree differencing algorithm for fine-grained source code change extraction. For that, we have improved the existing algorithm of Chawathe et al. for extracting changes in hierarchically structured data. Our algorithm detects changes by finding a match between nodes of the compared two abstract syntax trees and a minimum edit script. We can identify change types between program versions according to our taxonomy of source code changes. We evaluated our change distilling algorithm with a benchmark we developed that consists of 1,064 manually classified changes in 219 revisions from three different open source projects. We achieved significant improvements in extracting types of source code changes: our algorithm approximates the minimum edit script by 45% better than the original change extraction approach by Chawathe et al. We are able to find all occurring changes and almost reach the minimum conforming edit script, i.e., we reach a mean absolute percentage error of 34%, compared to 79% reached by the original algorithm. The paper describes both the change distilling and the results of our evaluation. Weniger anzeigen