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Zespół Redakcyjny
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Open Access
vol. 17, 4, 2023
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CC-BY 4.0
Pobierz cytowanie
Enhancing Code Review Efficiency – Automated Pull Request Evaluation using Natural Language Processing and Machine Learning
Przemysław Wincenty Zydroń 1
,
 
Jarosław Protasiewicz 1
 
 
 
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National Information Processing Institute al. Niepodległości 188 b, 00-608 Warszawa, Poland
 
 
Autor do korespondencji
Przemysław Wincenty Zydroń   

National Information Processing Institute al. Niepodległości 188 b, 00-608 Warszawa, Poland
 
 
Adv. Sci. Technol. Res. J. 2023; 17(4):162-167
DOI: https://doi.org/10.12913/22998624/169576
 
 
Artykuł (PDF)
 
SŁOWA KLUCZOWE
machine learning
software development
code quality
code review
pull request
 
DZIEDZINY
 
STRESZCZENIE
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
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