Reviewing rounds prediction for code patches

Yuan Huang, Xingjian Liang, Zhihao Chen, Nan Jia, Xiapu Luo, Xiangping Chen, Zibin Zheng, Xiaocong Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number7
Pages (from-to)1-40
JournalEmpirical Software Engineering
Volume27
Issue number1
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Code patch
  • Code review
  • Discriminative feature
  • Machine learning
  • Reviewing rounds

ASJC Scopus subject areas

  • Software

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