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

• Autorzy: Zydroń Przemysław Wincenty , Protasiewicz Jarosław

Identyfikatory

DOI

10.12913/22998624/169576

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2023
• Tom: Vol. 17, no 4
• Strony: 162--167
• Opis fizyczny: Bibliogr. 27 poz., fig.

Twórcy

autor

Zydroń Przemysław Wincenty

• National Information Processing Institute al. Niepodległości 188 b, 00-608 Warszawa, Poland

• https://orcid.org/0000-0003-2519-3331

autor

Protasiewicz Jarosław

• National Information Processing Institute al. Niepodległości 188 b, 00-608 Warszawa, Poland

Bibliografia

• 1. Lipcak, J., Rossi, B. A large-scale study on source code reviewer recommendation in 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Czech Republic. 2018, 378– 387.
• 2. Kim, J., Lee, E. Understanding review expertise of developers: A reviewer recommendation approach based on latent dirichlet allocation. Symmetry. 2018; 10: 114
• 3. Ye, X. Learning to rank reviewers for pull requests. IEEE Access. 2019; 7: 85382–85391.
• 4. Wang, Y., Wang, X., Jiang, Y., Liang, Y., Liu, Y. A code reviewer assignment model incor- porating the competence differences and participant preferences. Foundations of Computing and Decision Sciences. 2016; 41: 77–91.
• 5. Liao, Z. et al. TIRR: A code reviewer recommendation algorithm with topic model and reviewer influence in 2019 IEEE Global Communications Conference (GLOBECOM), United States. 2019; 1–6.
• 6. Yu, Y., Wang, H., Yin, G. & Wang, T. Reviewer recommendation for pull-requests in GitHub: What can we learn from code review and bug assignment? Information and Software Technology. 2016; 74: 204–218.
• 7. Chen, Q. et al. Code reviewer recommendation in tencent: practice, challenge, and direction in Proceedings of the 44th International Conference on Software Engineering: Software Engineering in Practice United States. 2022; 115–124.
• 8. Sajedi-Badashian, A., Stroulia, E. Vocabulary and time based bug-assignment: A recommender system for open-source projects. Software: Practice and Experience 2020; 50, 1539–1564.
• 9. Ye, X., Zheng, Y., Aljedaani, W., Mkaouer, M. W. Recommending pull request reviewers based on code changes. Soft Computing. 2021; 25: 5619–5632.
• 10. Zanjani, M.B., Kagdi, H., Bird, C. Automatically recommending peer reviewers in modern code review. IEEE Transactions on Software Engineering. 2015; 42: 530–543.
• 11. Kovalenko, V., Tintarev, N., Pasynkov, E., Bird, C., Bacchelli, A. Does reviewer recommenda- tion help developers? IEEE Transactions on Software Engineering. 2018; 46, 710–731.
• 12. Tecimer, K.A., Tu¨zu¨n, E., Dibeklioglu, H., Erdogmus, H. in Evaluation and Assessment in Software Engineering. 2021; 181–190.
• 13. Google. Code Review Developer Guid, https:// google.github.io/eng-practices/review/
• 14. Hu, Y., Wang, J., Hou, J., Li, S., Wang, Q. Is There A” Golden” Rule for Code Reviewer Recommendation?:—An Experimental Evaluation in 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) 2020; 497–508.
• 15. Thongtanunam, P. et al. Who should review my code? a file location-based code-reviewer recommendation approach for modern code review in 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER) United States. 2015; 141–150.
• 16. Xia, X., Lo, D., Wang, X., Yang, X. Who should review this change?: Putting text and file location analyses together for more accurate recommendations in 2015 IEEE international conference on software maintenance and evolution (ICSME) United States 2015; 261–270.
• 17. Chouchen, M., Ouni, A., Mkaouer, M. W., Kula, R. G. & Inoue, K. WhoReview: A multi-objective search-based approach for code reviewers recom- mendation in modern code review. Applied Soft Computing. 2021; 100: 106908 2021.
• 18. Al-Zubaidi, W.H.A., Thongtanunam, P., Dam, H.K., Tantithamthavorn, C., Ghose, A. Workload-aware reviewer recommendation using a multi-objective search-based approach in Pro- ceedings of the 16th ACM International Conference on Predictive Models and Data Analytics in Software Engineering 2020; 21–30.
• 19. Sülün, E., Tüzün, E., Dŏgrusöz, U. Rstrace+: Reviewer suggestion using software artifact traceability graphs. Information and Software Technology 2021; 130: 106455.
• 20. Hu, Y., Wang, J., Li, S., Hu, J., Wang, Q. Response Time Constrained Code Reviewer Recom- mendation. Journal of Software. 2020; 32: 3372–3387.
• 21. Badampudi, D., Unterkalmsteiner, M., Britto, R. Modern Code Reviews-A Survey of Literature and Practice. ACM Transactions on Software Engineering and Methodology, 2023.
• 22. Tecimer, K.A., Tu¨zu¨n, E., Moran, C., Erdogmus, H. Cleaning ground truth data in software task assignment. Information and Software Technology. 2022; 149: 106956.
• 23. Liao, Z. et al. Core-reviewer recommendation based on Pull Request topic model and collaborator social network. Soft Computing. 2020; 24: 5683–5693.
• 24. Bubeck, S. et al. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712, 2023;
• 25. Richards, T. B. Auto-GPT; https://github.com/ Significant-Gravitas/Auto-GPT 26. Microsoft. JARVIS; https://github.com/microsoft/ JARVIS
• 27. Park, J.S. et al. Generative Agents: Interactive Simulacra of Human Behavior. arXiv preprint arXiv:2304.03442,2023.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).