CRII: SHF: Improving the Retention of Newcomers in FLOSS Projects With Useful and Timely Code Reviews

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Although popular Free / Libre Open Source Software (FLOSS) projects (e.g., Apache, Linux, and Android) attract plenty of motivated volunteers, most of these prospective joiners fail to become long-term contributors. According to the results of recent studies, one of the most common barriers reported by the newcomers is delayed or unfair feedback for their submitted code changes. This barrier often stems from a newcomer's struggle to identify suitable reviewers, who are experienced in the change areas and are capable of writing useful reviews. Since newcomers often have to wait 2-6 times longer than a long-term contributor to get reviews for their changes, they often become frustrated and abandon their on-boarding efforts. To address this issue, this project aims to build RevRanker, an automated model that can suggest reviewers who can provide timely and useful reviews.Toward this goal, this project will first build a theoretical understanding of useful code reviews in FLOSS projects using a mixed research method. This understanding will be then used to train and evaluate RevEval, an automated model to predict the usefulness of code reviews. Using the RevEval model as well as leveraging multiple historical dimensions of the files under review, RevRanker will be developed and evaluated. Finally, a plugin will be developed to integrate the RevRanker model with a popular code-review management system as well as to evaluate its effectiveness.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.