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

Advances in science & technology, research journal, 2023-01, Vol.17 (4), p.162-167

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Nhan đề:
Enhancing Code Review Efficiency: Automated Pull Request Evaluation using Natural Language Processing and Machine Learning
Tác giả: Zydroń, Przemysław Wincenty ; Protasiewicz, Jarosław
Chủ đề: code quality ; code review ; machine learning ; pull request ; software development
Là 1 phần của: Advances in science & technology, research journal, 2023-01, Vol.17 (4), p.162-167
Mô tả: 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
Nơi xuất bản: Lublin University of Technology
Ngôn ngữ: English
Số nhận dạng: ISSN: 2080-4075
EISSN: 2299-8624
DOI: 10.12913/22998624/169576
Nguồn: DOAJ Directory of Open Access Journals

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