Enhancing Code Review Efficiency – Automated Pull Request Evaluation using Natural Language Processing and Machine Learning

Abstract

The practice of code review is crucial in software development to improve code quality and promote knowledge exchange among team members. It requires identifying qualified reviewers with the necessary expertise and experience to thoroughly examine modifications suggested in a pull request and improve the efficiency of the code review process. However, it can be costly and time-consuming for maintainers to manually assign suitable reviewers to each request for large-scale projects. To address this challenge, various techniques, including machine learning, heuristic-based algorithms, and social network analysis, have been employed to suggest reviewers for pull requests automatically

Keywords

• machine learning
• software development
• code quality
• code review
• pull request