Who should comment on this pull request? Analyzing attributes for more accurate commenter recommendation in pull-based development

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• 作者：Jing Jiang^a ; ^{jiangjing@buaa.edu.cn} ; Yun Yang^a ; ^{ayonel@qq.com} ; Jiahuan He^a ; ^{lightbot.johnson@gmail.com} ; Xavier Blanc^b ; ^{xavier.blanc@labri.fr} ; Li Zhang ; ^a ; ^{lily@buaa.edu.cn}
• 关键词：Commenter recommendation ; Reviewer recommendation ; Attribute selection ; Pull-based software development
• 刊名：Information and Software Technology
• 出版年：2017
• 出版时间：April 2017
• 年：2017
• 卷：84
• 期：Complete
• 页码：48-62
• 全文大小：1176 K
• 卷排序：84

文摘

Context: The pull-based software development helps developers make contributions flexibly and efficiently. Commenters freely discuss code changes and provide suggestions. Core members make decision of pull requests. Both commenters and core members are reviewers in the evaluation of pull requests. Since some popular projects receive many pull requests, commenters may not notice new pull requests in time, and even ignore appropriate pull requests.Objective:Our objective in this paper is to analyze attributes that affect the precision and recall of commenter prediction, and choose appropriate attributes to build commenter recommendation approach.Method:We collect 19,543 pull requests, 206,664 comments and 4817 commenters from 8 popular projects in GitHub. We build approaches based on different attributes, including activeness, text similarity, file similarity and social relation. We also build composite approaches, including time-based text similarity, time-based file similarity and time-based social relation. The time-based social relation approach is the state-of-the-art approach proposed by Yu et al. Then we compare precision and recall of different approaches.Results:We find that for 8 projects, the activeness based approach achieves the top-3 precision of 0.276, 0.386, 0.389, 0.516, 0.322, 0.572, 0.428, 0.402, and achieves the top-3 recall of 0.475, 0.593, 0.613, 0.66, 0.644, 0.791, 0.714, 0.65, which outperforms approaches based on text similarity, file similarity or social relation by a substantial margin. Moreover, the activeness based approach achieves better precision and recall than composite approaches. In comparison with the state-of-the-art approach, the activeness based approach improves the top-3 precision by 178.788%, 30.41%, 25.08%, 41.76%, 49.07%, 32.71%, 25.15%, 78.67%, and improves the top-3 recall by 196.875%, 36.32%, 29.05%, 46.02%, 43.43%, 27.79%, 25.483%, 79.06% for 8 projects.Conclusion:The activeness is the most important attribute in the commenter prediction. The activeness based approach can be used to improve the commenter recommendation in code review.