ARCHIVES
Original Article
Design and Development of an AI-Powered Code Review Assistant
Neha Bharti1
Dr. R. S. Pandey2
1 2 Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Jharkhand, India.
Published Online: March-April 2026
Pages: 229-234
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702027
References
1. McIntosh, S., Kamei, Y., Adams, B., & Hassan, A. E. (2014). The impact of code review coverage and code review participation on
software quality. Proceedings of the 11th Working Conference on Mining Software Repositories (MSR), 192–201.
2. Bacchelli, A., & Bird, C. (2013). Expectations, outcomes, and challenges of modern code review. Proceedings of the 35th International
Conference on Software Engineering (ICSE), 712–721.
3. Rigby, P. C., & Storey, M. A. (2011). Understanding broadcast based peer review on open source software projects. Proceedings of
ICSE, 541–550.
4. Sadowski, C., et al. (2018). Modern code review: A case study at Google. Proceedings of the 40th International Conference on Software
Engineering: Software Engineering in Practice, 181–190.
5. Tufano, M., Watson, C., Bavota, G., et al. (2019). An empirical study on learning bug-fixing patches in the wild via neural
machine translation. ACM Transactions on Software Engineering and Methodology, 28(4).
6. Feng, Z., et al. (2020). CodeBERT: A pre-trained model for programming and natural languages. Findings of EMNLP, 1536–1547.
7. Ahmad, W. U., Chakraborty, S., Ray, B., & Chang, K. W. (2021). Unified pre-training for program understanding and generation.
NAACL-HLT.
8. OpenAI. (2023). GPT-4 Technical Report. arXiv:2303.08774.
9. Nijkamp, E., et al. (2022). CodeGen: An open large language model for code generation. arXiv:2203.13474.
10. Li, Y., et al. (2023). StarCoder: May the source be with you! arXiv:2305.06161.
11. Pearce, H., Ahmad, B., Tan, B., et al. (2022). Asleep at the keyboard? Assessing the security of GitHub Copilot’s code contributions.
IEEE Symposium on Security and Privacy.
12. Beller, M., Gousios, G., & Zaidman, A. (2017). Oops, my tests broke the build: An explorative analysis of Travis CI with GitHub.
Proceedings of MSR.
13. Vasilescu, B., Yu, Y., Wang, H., & Devanbu, P. (2015). Quality and productivity outcomes relating to continuous integration in
GitHub. Proceedings of ESEC/FSE.
14. Parnin, C., et al. (2017). The JavaScript ecosystem: A survey. Proceedings of ESEC/FSE.
15. Spinellis, D. (2006). Code review practices: Lessons from the open source community. IEEE Software, 23(1), 72–79.
16. Johnson, B., Song, Y., Murphy-Hill, E., & Bowdidge, R. (2013). Why don’t software developers use static analysis tools to find bugs?
Proceedings of ICSE, 672–681.
17. GitHub. (2024). About pull requests and code review workflows. https://docs.github.com/en/pull-requests
18. Fowler, M. (2018). Continuous Integration. https://martinfowler.com/articles/continuousIntegration.html
software quality. Proceedings of the 11th Working Conference on Mining Software Repositories (MSR), 192–201.
2. Bacchelli, A., & Bird, C. (2013). Expectations, outcomes, and challenges of modern code review. Proceedings of the 35th International
Conference on Software Engineering (ICSE), 712–721.
3. Rigby, P. C., & Storey, M. A. (2011). Understanding broadcast based peer review on open source software projects. Proceedings of
ICSE, 541–550.
4. Sadowski, C., et al. (2018). Modern code review: A case study at Google. Proceedings of the 40th International Conference on Software
Engineering: Software Engineering in Practice, 181–190.
5. Tufano, M., Watson, C., Bavota, G., et al. (2019). An empirical study on learning bug-fixing patches in the wild via neural
machine translation. ACM Transactions on Software Engineering and Methodology, 28(4).
6. Feng, Z., et al. (2020). CodeBERT: A pre-trained model for programming and natural languages. Findings of EMNLP, 1536–1547.
7. Ahmad, W. U., Chakraborty, S., Ray, B., & Chang, K. W. (2021). Unified pre-training for program understanding and generation.
NAACL-HLT.
8. OpenAI. (2023). GPT-4 Technical Report. arXiv:2303.08774.
9. Nijkamp, E., et al. (2022). CodeGen: An open large language model for code generation. arXiv:2203.13474.
10. Li, Y., et al. (2023). StarCoder: May the source be with you! arXiv:2305.06161.
11. Pearce, H., Ahmad, B., Tan, B., et al. (2022). Asleep at the keyboard? Assessing the security of GitHub Copilot’s code contributions.
IEEE Symposium on Security and Privacy.
12. Beller, M., Gousios, G., & Zaidman, A. (2017). Oops, my tests broke the build: An explorative analysis of Travis CI with GitHub.
Proceedings of MSR.
13. Vasilescu, B., Yu, Y., Wang, H., & Devanbu, P. (2015). Quality and productivity outcomes relating to continuous integration in
GitHub. Proceedings of ESEC/FSE.
14. Parnin, C., et al. (2017). The JavaScript ecosystem: A survey. Proceedings of ESEC/FSE.
15. Spinellis, D. (2006). Code review practices: Lessons from the open source community. IEEE Software, 23(1), 72–79.
16. Johnson, B., Song, Y., Murphy-Hill, E., & Bowdidge, R. (2013). Why don’t software developers use static analysis tools to find bugs?
Proceedings of ICSE, 672–681.
17. GitHub. (2024). About pull requests and code review workflows. https://docs.github.com/en/pull-requests
18. Fowler, M. (2018). Continuous Integration. https://martinfowler.com/articles/continuousIntegration.html
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