A Tale of CI Build Failures: An Open Source and a Financial Organization Perspective

2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)

Panichella

Palomba

et al. 2018

Self Cite

Automatic static analysis tools (ASATs) are tools that support automatic code quality evaluation of software systems with the aim of (i) avoiding and/or removing bugs and (ii) spotting design issues. Hindering their widespread acceptance are their (i) high false positive rates and (ii) low comprehensibility of the generated warnings. Researchers and ASATs vendors have proposed solutions to prioritize such warnings with the aim of guiding developers toward the most severe ones. However, none of the proposed solutions considers the development context in which an ASAT is being used to further improve the selection of relevant warnings. To shed light on the impact of such contexts on the warnings configuration, usage and adopted prioritization strategies, we surveyed 42 developers (69% in industry and 31% in open source projects) and interviewed 11 industrial experts that integrate ASATs in their workflow. While we can confirm previous findings on the reluctance of developers to configure ASATs, our study highlights that (i) 71% of developers do pay attention to different warning categories depending on the development context, and (ii) 63% of our respondents rely on specific factors (e.g., team policies and composition) when prioritizing warnings to fix during their programming. Our results clearly indicate ways to better assist developers by improving existing warning selection and prioritization strategies.

Section: B Semi-structured Interviews With Professional Developerssupporting

confidence: 80%

Context is king: The developer perspective on the usage of static analysis tools

2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)

Panichella

Palomba

et al. 2018

Self Cite

“…In the last years, researchers have proposed a growing number of studies targeting CI practices [2,38,39], also thanks to the increasing availability of publicly available CI data [3].…”

Section: Related Workmentioning

confidence: 99%

Continuous code quality: are we (really) doing that?

Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering

Palomba

Bacchelli

et al. 2018

Self Cite

Continuous Integration (CI) is a software engineering practice where developers constantly integrate their changes to a project through an automated build process. The goal of CI is to provide developers with prompt feedback on several quality dimensions after each change. Indeed, previous studies provided empirical evidence on a positive association between properly following CI principles and source code quality. A core principle behind CI is Continuous Code Quality (also known as CCQ, which includes automated testing and automated code inspection) may appear simple and effective, yet we know little about its practical adoption. In this paper, we propose a preliminary empirical investigation aimed at understanding how rigorously practitioners follow CCQ. Our study reveals a strong dichotomy between theory and practice: developers do not perform continuous inspection but rather control for quality only at the end of a sprint and most of the times only on the release branch. Preprint [

“…Studying and proposing automated fixed for build failures has also been a topic of investigation. Previous work has investigated the phenomenon of build failures [22], [32] from different perspectives, such as testing [1] and code analysis [34]. Also, researchers have proposed fixes for some kinds of build failures, e.g., broken dependencies related [16], or proposed approaches to augment the comprehensibility of build logs while inspecting the cause of such failures [31].…”

Section: Related Workmentioning

confidence: 99%

Automated Reporting of Anti-Patterns and Decay in Continuous Integration

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

Proksch

Gall

et al. 2019

Self Cite

Continuous Integration (CI) is a widely-used software engineering practice. The software is continuously built so that changes can be easily integrated and issues such as unmet quality goals or style inconsistencies get detected early. Unfortunately, it is not only hard to introduce CI into an existing project, but it is also challenging to live up to the CI principles when facing tough deadlines or business decisions. Previous work has identified common anti-patterns that reduce the promised benefits of CI. Typically, these anti-patterns slowly creep into a project over time before they are identified. We argue that automated detection can help with early identification and prevent such a process decay. In this work, we further analyze this assumption and survey 124 developers about CI anti-patterns. From the results, we build CI-ODOR, a reporting tool for CI processes that detects the existence of four relevant anti-patterns by analyzing regular build logs and repository information. In a study on the 18,474 build logs of 36 popular JAVA projects, we reveal the presence of 3,823 high-severity warnings spread across projects. We validate our reports in a survey among 13 original developers of these projects and through general feedback from 42 developers that confirm the relevance of our reports.