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

• Main Authors: JIANG, Jing, LO, David, ZHENG, Jiateng, XIA, Xin, YANG, Yun, ZHANG, Li
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Integrator prediction; Code review; Open source; GitHub; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4345; https://ink.library.smu.edu.sg/context/sis_research/article/5348/viewcontent/jss191.pdf
• Institution: Singapore Management University

In pull-based development model, integrators are responsible for making decisions about whether to accept pull requests andintegrate 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 inprocessing 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 andFile 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 approachescHRev, WRC, TIE, CoreDevRec and ACRec, TRFPre improves top-1 accuracy by 68.2%, 73.9%, 49.3%, 14.3% and 46.4% onaverage across 24 projects.