EARec: Leveraging Expertise and Authority for Pull-Request Reviewer Recommendation in GitHub

摘要

Pull-Request (PR) is a primary way of code contribution from developers to improve quality of software projects in GitHub. For a popular GitHub project, tens of PR are submitted daily, while only a small number of developers, i.e core developers, have the grant to judge whether to merge these changes into the main branches or not. Due to the time-consumption of PR review and the diversity of PR aspects, it is becoming a big challenge for core developers to quickly discover the useful PR. Currently, recommending appropriate reviewers (developers) for incoming PR to quickly collect meaningful comments, is treated as an effective and crowdsourced way to help core developers to make decisions and thus accelerate project development. In this paper, we propose a reviewer recommendation approach (EARec) which simultaneously considers developer expertise and authority. Specifically, we first construct a graph of incoming PR and possible reviewers, and then take advantage of text similarity of PR and social relations of reviewers to find the appropriate reviewers. The experimental analysis on MSR Mining Challenge Datasetootnote{http://ghtorrent.org/msr14.html} provides good evaluation for our approach in terms of precision and recall.