Automated Code Reviewer Recommendation forPullRequests

Abstract

With the advent of distributed software development based on pull requests, it is possible to review code changes by a third party before integrating them into the master program in an informal and tool-based process called Modern Code Review (MCR). Effectively performing MCR can facilitate the software evolution phase by reducing post-release defects. MCR allows developers to invite appropriate reviewers to inspect their code once a pull request has been submitted. In many projects, selecting the right reviewer is time-consuming and challenging due to the high requests volume and potential reviewers. Various recommender systems have been proposed in the past that use heuristics, machine learning, or social networks to automatically suggest reviewers. Many previous approaches focus on a narrow set of features of candidate reviewers, including their reviewing expertise, and some have been evaluated on small datasets that do not provide generalizability. Additionally, it is common for them not to meet the desired accuracy, precision, or recall standards. Aim: Our aim is to increase the accuracy of code reviewer recommendations by calculating scores relatively and considering the importance of the recency of activities in an optimal way. Method: Our work presents a heuristic approach that takes into account both candidate reviewers’ expertise in reviewing and committing, as well as their social relations to automatically recommend code reviewers. During the development of the approach, we will examine how each of the reviewers’ features contributes to their suitability to review the new request. Results: We evaluated our algorithm on five open-source projects from GitHub. Results indicate that, based on top-1 accuracy, 3-top accuracy, and mean reciprocal rank, our proposed approach achieves 46%, 75%, and 62% values respectively, outperforming previous related works. Conclusion: These results indicate that combining different features of reviewers, including their expertise level and previous collaboration history, can lead to better code reviewer recommendations, as demonstrated by the achieved improvements over previous related works.