Who Will be Interested in? A Contributor Recommendation Approach for Open Source Projects

Xunhui Zhang, Tao Wang, Gang Yin, Cheng Yang, Huaimin Wang
2017 Proceedings of the 29th International Conference on Software Engineering and Knowledge Engineering  
The crowds' continuous participation and contribution are the key factors for the success of open source projects. However, among the massive competitors, it is difficult for a project to attract enough contributors by just passively waiting for enthusiasts to join in. Instead, it should actively seek gifted developers. Most of the current studies mainly focus on recommending experts inside a repository for some specific development tasks. In this paper, we propose a novel approach ConRec to
more » ... proach ConRec to recommend potential contributors across the entire open source community for given projects. It leverages the developers' historical activities in projects to analyze their technical interests and technical connections with others. Thereafter, it combines collaborative filtering algorithm with text matching algorithm to recommend proper developers. We conducted extensive experiments on 5,995 open source projects and 2,938,620 developers in GitHub. The results show that the proposed algorithm can recommend contributors to open source projects with the best performance of 63% in accuracy, and solve the cold start problem as well. • We design a commit network to measure the collaboration connections between developers. Based on this network,
doi:10.18293/seke2017-067 dblp:conf/seke/ZhangWYYW17 fatcat:klf4waxyqzbfzhmxpn6dexo42y