Integrating Visual Aids to Enhance the Code Reviewer Selection Process

• Published in: 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME) pp. 293 - 305
• Main Authors: Rahman, Md Shamimur, Mondal, Debajyoti, Codabux, Zadia, Roy, Chanchal K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2023
• Subjects: Code Review; Codes; Collaboration; Developer Workload; Manuals; Measurement; Network Visualization; Reviewer Recommendation; Software maintenance; Surveys; Visualization
• ISSN: 2576-3148
• DOI: 10.1109/ICSME58846.2023.00037

Modern Code Review (MCR) is an integral part of a software development strategy that accelerates product quality by identifying defects, code smells, and other harmful practices. However, assigning appropriate reviewers to evaluate changed code during the review process remains challenging. While automated tools for reviewer assignments have limited impact in practice, the process often relies on manual investigation of project histories to retrieve knowledge of team members and their activities. Therefore, in this study, we present an approach to automatically assemble developers' information and visualize it meaningfully, which helps to choose appropriate reviewers. First, we propose three metrics that measure developers' collaboration, reviewers' expertise, and reviewers' workload and visualize them through networks. Second, we perform a case study of three popular open-source projects, where we compute and visualize each developers' information according to the proposed metrics. Finally, we conducted two online surveys to assess the developers' perceptions of the proposed visual benefits. The results show that the proposed method can assist in identifying relevant reviewers and be immensely helpful to new developers. Additionally, survey respondents expressed reliance on the efficacy of the visual aids in workload balancing and reducing review time.