Core-reviewer recommendation based on Pull Request topic model and collaborator social network
Author
dc.contributor.author
Liao, Zhifang
Author
dc.contributor.author
Wu, ZeXuan
Author
dc.contributor.author
Li, Yanbing
Author
dc.contributor.author
Zhang, Yan
Author
dc.contributor.author
Fan, Xiaoping
Author
dc.contributor.author
Wu, Jinsong
Admission date
dc.date.accessioned
2020-04-22T22:51:47Z
Available date
dc.date.available
2020-04-22T22:51:47Z
Publication date
dc.date.issued
2020
Cita de ítem
dc.identifier.citation
Soft Computing (2020) 24:5683–5693.
es_ES
Identifier
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10.1007/s00500-019-04217-7
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/174030
Abstract
dc.description.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%.