Predicting long-time contributors for GitHub projects using machine learning

Many organizations develop software systems using open source software (OSS), which is risky due to the high possibility of losing support. Contributors are critical for the survival of OSS projects, but very few new contributors remain with OSS projects to become long-time contributors (LTCs). Identification of factors that contribute to become an LTC can help OSS project owners utilize limited resources to retain new contributors. In this paper, we investigate whether we can effectively predict new contributors to OSS repositories becoming long time contributors based on repository and contributor meta-data collected from GitHub. We construct a dataset containing 70,899 observations from 888 most popular repositories with 56,766 contributors.