Poster: A Recommender System for Developer Onboarding

Successfully onboarding open source projects in GitHub is difficult for developers, because it is time-consuming for them to search an expected project by a few query words from numerous repositories, and developers suffer from various social and technical barriers in joined projects. Frequently failed onboarding postpones developers' development schedule, and the evolutionary progress of open source projects. To mitigate developers' costly efforts for onboarding, we propose a ranking model NNLRank (Neural Network for List-wise Ranking) to recommend projects that developers are likely to contribute many commits. Based on 9 measured project features, NNLRank learns a ranking function (represented by a neural network, optimized by a list-wise ranking loss function) to score a list of candidate projects, where top-n scored candidates are recommended to a target developer. We evaluate NNLRank by 2044 succeeded onboarding decisions from GitHub developers, comparing with a related model LP (Link Prediction), and 3 other typical ranking models. Results show that NNLRank can provide developers with effective recommendation, substantially outperforming baselines.