Abstract
Pull Request (PR) is a major contributor to external developers of open-source projects in GitHub. PR reviewing is an important part of open-source software developments to ensure the quality of project. Recommending suitable candidates of reviewer to the new PRs will make the PR reviewing more efficient. However, there is not a mechanism of automatic reviewer recommendation for PR in GitHub. In this paper, we propose an automatic core-reviewer recommendation approach, which combines PR topic model with collaborators in the social network. First PR topics will be extracted from PRs by the latent Dirichlet allocation, and then the collaborator–PR network will be constructed with the connection between collaborators and PRs, and the influence of each collaborator will be calculated via the improved PageRank algorithm which combines with HITS. Finally, the relationship between topics and collaborators will also be built by the history of PR reviewing. When a new PR presents, a collaborator will be chosen as a core reviewer according to the influence of collaborators and the relationship between the new PR and collaborators. The experiment results show in the matching score calculation processing, the influence of collaborators shows higher than that with the expert, and the recommendation precision is better than 70%.
Highlights
GitHub, a popular open-source community (Begel et al 2013; Liao et al 2018), has attracted the participations of tens of millions of developers and millions of open-source projects
We propose an automatic core-reviewer recommendation approach, which combines Pull Request (PR) topic model with collaborators in the social network
We propose a NTCRA (Social Network and Topic Model-based Core-Reviewer Recommendation Algorithm) algorithm to match the appropriate collaborators as the reviewer for new PR
Summary
GitHub, a popular open-source community (Begel et al 2013; Liao et al 2018), has attracted the participations of tens of millions of developers and millions of open-source projects. To improve the efficiency of PR reviewing, some researchers (Yu et al 2014; Balachandran 2013; Thongtanunam et al 2014) have proposed some proposals to recommend appropriate reviewers for new PR. The reviewers they recommend include any developers, whatever the core developer or external developer he or she is. If the recommender is the core developer, the delay can be reduced greatly
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