Enhancing Code Review Efficiency – Automated Pull Request Evaluation using Natural Language Processing and Machine Learning
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 improv...
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| Format: | Article |
| Language: | English |
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Lublin University of Technology
2023-08-01
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| Series: | Advances in Sciences and Technology |
| Subjects: | |
| Online Access: | http://www.astrj.com/Enhancing-Code-Review-Efficiency-Automated-Pull-Request-Evaluation-using-Natural,169576,0,2.html |
| _version_ | 1797742273782349824 |
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| author | Przemysław Wincenty Zydroń Jarosław Protasiewicz |
| author_facet | Przemysław Wincenty Zydroń Jarosław Protasiewicz |
| author_sort | Przemysław Wincenty Zydroń |
| collection | DOAJ |
| description | 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 |
| first_indexed | 2024-03-12T14:39:30Z |
| format | Article |
| id | doaj.art-d05757131afe4f31a0b4370f9560228b |
| institution | Directory Open Access Journal |
| issn | 2080-4075 2299-8624 |
| language | English |
| last_indexed | 2024-03-12T14:39:30Z |
| publishDate | 2023-08-01 |
| publisher | Lublin University of Technology |
| record_format | Article |
| series | Advances in Sciences and Technology |
| spelling | doaj.art-d05757131afe4f31a0b4370f9560228b2023-08-16T16:30:50ZengLublin University of TechnologyAdvances in Sciences and Technology2080-40752299-86242023-08-0117416216710.12913/22998624/169576169576Enhancing Code Review Efficiency – Automated Pull Request Evaluation using Natural Language Processing and Machine LearningPrzemysław Wincenty Zydroń0https://orcid.org/0000-0003-2519-3331Jarosław Protasiewicz1National Information Processing Institute al. Niepodległości 188 b, 00-608 Warszawa, PolandNational Information Processing Institute al. Niepodległości 188 b, 00-608 Warszawa, PolandThe 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 automaticallyhttp://www.astrj.com/Enhancing-Code-Review-Efficiency-Automated-Pull-Request-Evaluation-using-Natural,169576,0,2.htmlmachine learningsoftware developmentcode qualitycode reviewpull request |
| spellingShingle | Przemysław Wincenty Zydroń Jarosław Protasiewicz Enhancing Code Review Efficiency – Automated Pull Request Evaluation using Natural Language Processing and Machine Learning Advances in Sciences and Technology machine learning software development code quality code review pull request |
| title | Enhancing Code Review Efficiency – Automated Pull Request Evaluation using Natural Language Processing and Machine Learning |
| title_full | Enhancing Code Review Efficiency – Automated Pull Request Evaluation using Natural Language Processing and Machine Learning |
| title_fullStr | Enhancing Code Review Efficiency – Automated Pull Request Evaluation using Natural Language Processing and Machine Learning |
| title_full_unstemmed | Enhancing Code Review Efficiency – Automated Pull Request Evaluation using Natural Language Processing and Machine Learning |
| title_short | Enhancing Code Review Efficiency – Automated Pull Request Evaluation using Natural Language Processing and Machine Learning |
| title_sort | enhancing code review efficiency automated pull request evaluation using natural language processing and machine learning |
| topic | machine learning software development code quality code review pull request |
| url | http://www.astrj.com/Enhancing-Code-Review-Efficiency-Automated-Pull-Request-Evaluation-using-Natural,169576,0,2.html |
| work_keys_str_mv | AT przemysławwincentyzydron enhancingcodereviewefficiencyautomatedpullrequestevaluationusingnaturallanguageprocessingandmachinelearning AT jarosławprotasiewicz enhancingcodereviewefficiencyautomatedpullrequestevaluationusingnaturallanguageprocessingandmachinelearning |