Pull Request Prioritization Algorithm based on Acceptance and Response Probability

Abstract

Pull requests (PRs) prioritization is one of the main challenges faced by integrators in pull-based development. This is especially true for large open-source projects where hundreds of pull requests are submitted daily. Indeed, managing these pull requests manually consumes time and resources and may lead to delays in the reaction (i.e., acceptance or response) to enhancements or bug fixes suggested in the codebase by contributors. We propose an approach, called AR-Prioritizer (Acceptance and Response based Prioritizer), integrating a PRs prioritization mechanism that considers these two aspects. The results of our study demonstrate that our approach can recommend top@5, top@10, and top@20 most likely to be accepted and responded pull requests with Mean Average Precision of 95.3%, 89.6%, and 79.6% and Average Recall of 40%, 65.7%, and 92.9%. Moreover, AR-Prioritizer has outperformed the baseline models with a statistical significance in prioritizing the most likely to be accepted and responded to PRs.