Reviewing rounds prediction for code patches

Code review is one of the common activities to guarantee the reliability of software, while code review is time-consuming as it requires reviewers to inspect the source code of each patch. A patch may be reviewed more than once before it is eventually merged or abandoned, and then such a patch may t...

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Bibliographic Details
Published in:Empirical software engineering : an international journal 2022-01, Vol.27 (1), Article 7
Main Authors: Huang, Yuan, Liang, Xingjian, Chen, Zhihao, Jia, Nan, Luo, Xiapu, Chen, Xiangping, Zheng, Zibin, Zhou, Xiaocong
Format: Article
Language:English
Subjects:
Case studies
Collaboration
Compilers
Computer Science
Inspection
Interpreters
Machine learning
Methods
Natural language
Programming Languages
Recommendation Systems for Software Engineering
Reviewing
Software engineering
Software Engineering/Programming and Operating Systems
Software reliability
Source code
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Summary:Code review is one of the common activities to guarantee the reliability of software, while code review is time-consuming as it requires reviewers to inspect the source code of each patch. A patch may be reviewed more than once before it is eventually merged or abandoned, and then such a patch may tighten the development schedule of the developers and further affect the development progress of a project. Thus, a tool that predicts early on how long a patch will be reviewed can help developers take self-inspection beforehand for the patches that require long-time review. In this paper, we propose a novel method, PMCost , to predict the reviewing rounds of a patch. PMCost uses a number of features, including patch meta-features, code diff features, personal experience features and patch textual features, to better reflect code changes and review process. To examine the benefits of PMCost , we perform experiments on three large open source projects, namely Eclipse, OpenDaylight and OpenStack. The encouraging experimental results demonstrate the feasibility and effectiveness of our approach. Besides, we further study the why the proposed features contribute to the reviewing rounds prediction.
ISSN:1382-3256
1573-7616
DOI:10.1007/s10664-021-10035-z