Core-reviewer recommendation based on Pull Request topic model and collaborator social network

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%.

How to Construct a Power Knowledge Graph with Dispatching Data?

“…At present, many wellknown knowledge graph projects organize a large amount of data, extract knowledge from them for organization and management, and provide users with high-quality intelligent services, such as understanding the semantics of search and providing more accurate search answers. In recent years, due to the development of crowdsourcing [4] and open-source ecosystems [5][6][7], the related research of constructing knowledge graphs by crowdsourcing and knowledge graphs in software has become a new research topic in the field of knowledge graphs, which also shows that knowledge graphs are flexible to organize domain knowledge.…”

Expanding the Number of Reviewers in Open-Source Projects by Recommending Appropriate Developers

“…Machine learning-based approaches use machine learning techniques and require building a model based on a training set [59]. Social network-based approaches identify various relationships among contributors to suggest reviewers [60], [61], [62]. Hybrid approaches rely on various combinations of techniques [63], [64], [65], [66].…”

“…For instance, Yu et al [60], [61] provide several recommender systems built around analyzing a network of contributors and their comments on pull requests. In contrast, Liao et al [62] construct a network of collaborators and pull request topics.…”

Smart Collaborative Learning

“…This is why collaborative strategies consider smart learner as an important part of team building that is focused on the profile and abilities of learner. In order to group it under knowledge, skills, attitudes, and values (Liao et al, 2019). The participation of each learner takes into account accessibility and capabilities and requires a deep understanding of profile member in order to create a complementary participation between each member of team.…”