Four eyes are better than two: On the impact of code reviews on software quality

Bavota, Gabriele; Russo, Barbara

doi:10.1109/icsm.2015.7332454

Cited by 70 publications

(50 citation statements)

References 29 publications

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“…These results support the observations of Bavota et al [4], as they also were not able to find any link between the number of comments and the chance of defects slipping through review.…”

Section: Discussionsupporting

confidence: 94%

“…The issue discussion metrics are mined from issue repository comments, hence we calculate them on a per-issue report (and hence per-commit) basis 4 . The review discussion metrics are mined from the Gerrit reviews and comments, and hence are calculated on a per-review basis.…”

Section: Discussion Metricsmentioning

confidence: 99%

“…Bavota et al [4] empirically showed on three large systems that code reviewing significantly decreases the chance of defect introduction, while it also substantially increases the readability of the reviewed code. Similar to us, they first had to link commits to reviews to determine which commits were reviewed and which ones not.…”

Section: Related Workmentioning

confidence: 98%

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The Impact of Human Discussions on Just-in-Time Quality Assurance: An Empirical Study on OpenStack and Eclipse

Tourani

Adams

2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

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In order to spot defect-introducing code changes during review before they are integrated into a project's version control system, a variety of defect prediction models have been designed. Most of these models focus exclusively on source code properties, like the number of added or deleted lines, or developer-related measures like experience. However, a code change is only the outcome of a much longer process, involving discussions on an issue report and review discussions on (different versions of) a patch. Similar to how body language implicitly can reveal a person's real feelings, the length, intensity or positivity of these discussions can provide important additional clues about how risky a particular patch is or how confident developers and reviewers are about the patch. In this paper, we build logistic regression models to study the impact of the characteristics of issue and review discussions on the defect-proneness of a patch. Comparison of these models to conventional source code-based models shows that issue and review metrics combined improve precision and recall of the explanatory models up to 10%. Review time and issue discussion lag are amongst the most important metrics, having a positive (i.e., increasing) relation with defectproneness.

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“…These results support the observations of Bavota et al [4], as they also were not able to find any link between the number of comments and the chance of defects slipping through review.…”

Section: Discussionsupporting

confidence: 94%

Section: Discussion Metricsmentioning

confidence: 99%

See 1 more Smart Citation

The Impact of Human Discussions on Just-in-Time Quality Assurance: An Empirical Study on OpenStack and Eclipse

Tourani

Adams

2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

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“…Several studies have investigated the impact that poor review participation can have on software quality and code review process. Bavota and Russo (2015) find that a patch with lower review participation has a higher chance of inducing bug fixes. McIntosh et al (2014) find that the lack of review participation has a negative impact on software quality.…”

Section: Background and Research Questionsmentioning

confidence: 81%

The impact of human factors on the participation decision of reviewers in modern code review

et al. 2018

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Modern Code Review (MCR) plays a key role in software quality practices. In MCR process, a new patch (i.e., a set of code changes) is encouraged to be examined by reviewers in order to identify weaknesses in source code prior to an integration into main software repositories. To mitigate the risk of having future defects, prior work suggests that MCR should be performed with sufficient review participation. Indeed, recent work shows that a low number of participated reviewers is associated with poor software quality. However, there is a likely case that a new patch still suffers from poor review participation even though reviewers were invited. Hence, in this paper, we set out to investigate the factors that are associated with the participation decision of an invited reviewer. Through a case study of 230,090 patches spread across the Android, LibreOffice, OpenStack and Qt systems, we find that (1) 16%-66% of patches have at least one invited reviewer who did not respond to the review invitation; (2) human factors play an important role in predicting whether or not an invited reviewer will participate in a review; (3) a review participation rate of an invited reviewers and code authoring experience of an invited reviewer are highly associated with the participation decision of an invited reviewer. These results can help practitioners better understand about how human factors associate with the participation decision of reviewers and serve as guidelines for inviting reviewers, leading to a better inviting decision and a better reviewer participation.

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“…Some educators use static analysis tools such as Checkstyle 1 or FindBugs 2 to support the assessment of programming assignments. For example, Keuning et al [18] recently showed that many student programs in the (huge) Blackbox database contain quality issues as reported by PMD 3 , and that students hardly ever fix an issue, even when they make use of analysis tools. These tools check a large range of potential quality issues, but it is not clear which of these tools are suitable or even appropriate in an educational context.…”

Section: Introductionmentioning

confidence: 99%

"I know it when I see it" Perceptions of Code Quality

Börstler

Störrle²,

Toll

et al. 2018

Proceedings of the 2017 ITiCSE Conference on Working Group Reports

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Context. Code quality is a key issue in software development. The ability to develop high quality software is therefore a key learning goal of computing programs. However, there are no universally accepted measures to assess the quality of code and current standards are considered weak. Furthermore, there are many facets to code quality. Defining and explaining the concept of code quality is therefore a challenge faced by many educators. Objectives. In this working group, we investigated code quality as perceived by students, educators, and professional developers, in particular, the differences in their views of code quality and which quality aspects they consider as more or less important. Furthermore, we investigated their sources for information about code quality and its assessment. Methods. We interviewed 34 students, educators and professional developers regarding their perceptions of code quality. For the interviews they brought along code from their own experience to discuss and exemplify code quality.Results. There was no common definition of code quality among * Working group co-leaders:

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Four eyes are better than two: On the impact of code reviews on software quality

Cited by 70 publications

References 29 publications

The Impact of Human Discussions on Just-in-Time Quality Assurance: An Empirical Study on OpenStack and Eclipse

The Impact of Human Discussions on Just-in-Time Quality Assurance: An Empirical Study on OpenStack and Eclipse

The impact of human factors on the participation decision of reviewers in modern code review

"I know it when I see it" Perceptions of Code Quality

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