CORMS: A GitHub and Gerrit based Hybrid Code Reviewer Recommendation Approach for Modern Code Review

Abstract

Modern Code review (MCR ) techniques are widely adopted in both open source software platforms and organizations to ensure the quality of their software products. However, the selection of reviewers for code review is cumbersome with the increasing size of development teams. The recommendation of inappropriate reviewers for code review can take more time and effort to complete the task effectively. We carried out a detailed literature review over existing recommendation approaches and extended the baseline of reviewers� recommendation framework RevFinder1 to handle issues with newly created files, retired reviewers, the external validity of results, and the accuracies of the state of theart RevFinder. Our proposed hybrid approach, CORMS, works on similarity analysis to compute similarities among filepaths, projects/subprojects, author information, and prediction models to recommend reviewers based on the subject of the change. We conducted a detailed analysis on the widely used 20 projects of both Gerrit and GitHub to compare our results with RevFinder. Our results reveal that on average, CORMS, can achieve top-10, top-5, top-3, and top-1 accuracies, and Mean Reciprocal Rank (MRR) of 79.9%, 74.6%, 67.5%, 45.1% and 0.58 for the 20 projects, consequently improves the RevFinder approach by 12.3%, 20.8%, 34.4%, 44.9% and 18.4%, respectively. Finally, we built a complete tool CORMSTOOL based on our proposed approach, CORMS, to support reviewer recommendation process in modern code review.