Who should make decision on this pull request? Analyzing time-decaying relationships and file similarities for integrator prediction

Jing Jiang, David Lo, Jiateng Zheng, Xin Xia, Yun Yang, Li Zhang

Research output: Contribution to journalArticleResearchpeer-review

Abstract

In pull-based development model, integrators are responsible for making decisions about whether to accept pull requests and integrate code contributions. Ideally, pull requests are assigned to integrators and evaluated within a short time after their submissions. However, the volume of incoming pull requests is large in popular projects, and integrators often encounter difficulties in processing pull requests in a timely fashion. Therefore, an automatic integrator prediction approach is required to assign appropriate pull requests to integrators. In this paper, we propose an approach TRFPre which analyzes Time-decaying Relationships and File similarities to predict integrators. We evaluate the effectiveness of TRFPre on 24 projects containing 138,373 pull requests. Experimental results show that TRFPre makes accurate integrator predictions in terms of accuracies and Mean Reciprocal Rank. Less than 2 predictions are needed to find correct integrator in 91.67% of projects. In comparison with state-of-the-art approaches cHRev, WRC, TIE, CoreDevRec and ACRec, TRFPre improves top-1 accuracy by 68.2%, 73.9%, 49.3%, 14.3% and 46.4% on average across 24 projects.

Original languageEnglish
Pages (from-to)196-210
Number of pages15
JournalJournal of Systems and Software
Volume154
DOIs
Publication statusPublished - Aug 2019

Keywords

  • Code review
  • Github
  • Integrator prediction
  • Open source

Cite this

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title = "Who should make decision on this pull request? Analyzing time-decaying relationships and file similarities for integrator prediction",
abstract = "In pull-based development model, integrators are responsible for making decisions about whether to accept pull requests and integrate code contributions. Ideally, pull requests are assigned to integrators and evaluated within a short time after their submissions. However, the volume of incoming pull requests is large in popular projects, and integrators often encounter difficulties in processing pull requests in a timely fashion. Therefore, an automatic integrator prediction approach is required to assign appropriate pull requests to integrators. In this paper, we propose an approach TRFPre which analyzes Time-decaying Relationships and File similarities to predict integrators. We evaluate the effectiveness of TRFPre on 24 projects containing 138,373 pull requests. Experimental results show that TRFPre makes accurate integrator predictions in terms of accuracies and Mean Reciprocal Rank. Less than 2 predictions are needed to find correct integrator in 91.67{\%} of projects. In comparison with state-of-the-art approaches cHRev, WRC, TIE, CoreDevRec and ACRec, TRFPre improves top-1 accuracy by 68.2{\%}, 73.9{\%}, 49.3{\%}, 14.3{\%} and 46.4{\%} on average across 24 projects.",
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Who should make decision on this pull request? Analyzing time-decaying relationships and file similarities for integrator prediction. / Jiang, Jing; Lo, David; Zheng, Jiateng; Xia, Xin; Yang, Yun; Zhang, Li.

In: Journal of Systems and Software, Vol. 154, 08.2019, p. 196-210.

Research output: Contribution to journalArticleResearchpeer-review

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AU - Zhang, Li

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