An Empirical Study of Cross-Project Pull Request Recommendation in GitHub

As a core contribution merge mechanism in distributed collaborative development, pull requests contain valuable knowledge of code evolution and issue resolution. With the co-evolution of multiple projects in a software ecosystem, relevant and similar issues can arise across different projects. Leveraging existing solutions in pull requests (PRs) through cross-project pull request recommendation (CPR) can enrich context knowledge and improve the efficiency of issue resolution. However, the characteristics of CPR and its effectiveness in the process of issue resolution still remain unclear. To bridge this gap, we conduct an empirical study of the CPR on GitHub. We first extract 4,445 CPRs from 2,500 open source projects and quantitatively analyze the characteristics of CPR. Then we conduct a qualitative analysis of sampled CPR cases to understand the influence of CPR. We also use a regression model to explore the impact of CPRs on issue resolution. Our main findings are as follows: (1) Experienced contributors in target projects make most of the CPRs and their CPRs are more timely than inexperienced contributors; (2) In CPR dataset, bugs constitute the largest proportion of target issue types, followed by enhancements, features and questions; (3) Nearly half of the CPRs are accepted by issue participants; (4) A greater number of the CPRs contribute indirectly to solving the target issue by offering solutions and contextual information, rather than providing appropriate code that can be directly applied to the issue; (5) Most of CPR-related factors have a significant impact on issue resolution delay. Among these, recommendation latency has the most significant impact, followed by the type of recommender. Our work has important insights into CPR and offers important guidance for developers on recommending cross-project PRs to resolve the mushrooming issues.