Investigating technical and non-technical factors influencing modern code review

Baysal, Olga; Kononenko, Oleksii; Holmes, Reid; Godfrey, Michael W.

doi:10.1007/s10664-015-9366-8

Cited by 92 publications

(75 citation statements)

References 11 publications

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“…For example, one unit increase in the owner's reputation (e.g., 0.1) will cause 1224% increase in odds of the patch getting accepted for Android Platform. Our finding is consistent with [19].…”

Section: Bugzillasupporting

confidence: 92%

“…For selecting features we used some features from [17,19] and [44] and we extends the features. Features such as severity, owner bug assignee match, file size, entropy score, number of assigned and contributing reviewers, and assigned reviewer's reputation are not investigated at [17,19].…”

Section: Features Used In the Studymentioning

confidence: 99%

“…Features such as severity, owner bug assignee match, file size, entropy score, number of assigned and contributing reviewers, and assigned reviewer's reputation are not investigated at [17,19]. The only feature which has been investigated in [44] is the entropy score.…”

Section: Features Used In the Studymentioning

confidence: 99%

“…Clearly, MCR is quite human intensive, which may require a substantial amount of effort [17], [50], [44], [19]. Also, the predictive recommendation and assessment of whether a patch will be eventually accepted or not as soon as it is submitted for MCR were not investigated previously.…”

Section: Introductionmentioning

confidence: 99%

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Effective assignment and assistance to software developers and reviewers

Zanjani

2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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DEDICATIONTo my parents and my sister. In this dissertation, we first present the automated solutions for Software Change Impact Analysis based on interaction and code review activities of developers. Then we provide explanation for two separate developer expertise models which use the micro-levels of human-to-code and human-to-human interactions from the previous code review and interaction activities of developers. Next, we present a reviewer expertise model based on code review activities of developers and show how this expertise model can be used for Code Reviewer Recommendation. At the end we examine the influential features that characterize the acceptance probability of a submitted patch (implemented code change) by developers. We present a predictive model that classifies whether a patch will be accepted or not as soon as it is submitted for code review in order to assist developers and reviewers in prioritizing and focusing their efforts.A rigorous empirical validation on large open source and commercial systems shows that the solutions based on the presented methodology outperform several existing solutions. The quantitative gains of our solutions across a spectrum of evaluation metrics along with their statistical significance are reported.vi

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Section: Bugzillasupporting

confidence: 92%

Section: Features Used In the Studymentioning

confidence: 99%

Section: Features Used In the Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effective assignment and assistance to software developers and reviewers

Zanjani

2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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“…Finally, this special section concludes with "Investigating technical and non-technical factors influencing modern code review" (Baysal et al 2016), in which the code review process of two large open-source projects is studied, in order to better understand the factors that might result in whether the code submissions are evaluated in a timely manner or not. Reverse engineering techniques are applied in order to reconstruct the patch submission process from the information in the issue tracking and code review systems.…”

mentioning

confidence: 99%

Guest editorial: special section on software reverse engineering

2016

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As defined in a seminal taxonomy by Chikofsky and Cross (Chikofsky and Cross II 1990) software reverse engineering is: "the process of analyzing a subject system to (i) identify the systems components and their inter-relationships and (ii) create representations of the system in another form or at a higher level of abstraction."Such an analysis process pertains to artifacts that can be at an extremely low-level, e.g., binary files or system execution traces, or at intermediate-level, e.g., such as source code, patches, and defects, and the history and discussions associated to these artifacts.Software reverse engineering is a mature research field with high practical relevance: often, the only way to get an understanding of a large and complex software system is through these lower-level artifacts, especially when higher-level artifacts are absent or

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