Recommending GitHub Projects by Leveraging Developers' Social Networks and Genetic Algorithm

Developers in GitHub continuously seek new opportunities to contribute to new projects. So far, several researchers have used users' historical activities, textual descriptions of projects or starred items to analyze or infer the interests or programming expertise of developers to offer possible project recommendations. While some research utilized social connections to obtain developers' social importance, none of them have implemented such aspects for generating project recommendations. In this research, we use the latest GHTorrent dataset to construct and propose a GitHub project recommendation system by leveraging Parallel Genetic Algorithm and developers' social networks. To the best of our knowledge, this is the first application of Genetic Algorithm in the GitHub project recommendation area.