Early prediction of merged code changes to prioritize reviewing tasks

Yuanrui Fan, Xin Xia, David Lo, Shanping Li

Research output: Contribution to journalArticleResearchpeer-review

18 Citations (Scopus)

Abstract

Modern Code Review (MCR) has been widely used by open source and proprietary software projects. Inspecting code changes consumes reviewers much time and effort since they need to comprehend patches, and many reviewers are often assigned to review many code changes. Note that a code change might be eventually abandoned, which causes waste of time and effort. Thus, a tool that predicts early on whether a code change will be merged can help developers prioritize changes to inspect, accomplish more things given tight schedule, and not waste reviewing effort on low quality changes. In this paper, motivated by the above needs, we build a merged code change prediction tool. Our approach first extracts 34 features from code changes, which are grouped into 5 dimensions: code, file history, owner experience, collaboration network, and text. And then we leverage machine learning techniques such as random forest to build a prediction model. To evaluate the performance of our approach, we conduct experiments on three open source projects (i.e., Eclipse, LibreOffice, and OpenStack), containing a total of 166,215 code changes. Across three datasets, our approach statistically significantly improves random guess classifiers and two prediction models proposed by Jeong et al. (2009) and Gousios et al. (2014) in terms of several evaluation metrics. Besides, we also study the important features which distinguish merged code changes from abandoned ones.

Original languageEnglish
Pages (from-to)3346-3393
Number of pages48
JournalEmpirical Software Engineering
Volume23
Issue number6
DOIs
Publication statusPublished - Dec 2018

Keywords

  • Code review
  • Features
  • Predictive model

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