TR
EN
The Evolution of Code Review: From Traditional Methods to AI-Powered Approaches
Öz
Code review is an important process in the software development lifecycle to ensure high-quality, maintainable, and efficient code. Over time, automated code review systems have evolved from manual methods to include advanced techniques such as dynamic analysis, machine learning, deep learning, and, more recently, generative artificial intelligence. These techniques use natural language processing and predictive models to provide faster and more accurate code reviews with intelligent, context-sensitive feedback. This survey explores the evolution of automated code review, starting with traditional methods and moving toward the latest AI-powered approaches. We discuss the methods used, the challenges faced, and the benefits these systems offer. In addition, we highlight future trends and examine how AI is transforming code review processes to meet the needs of modern software development.
Anahtar Kelimeler
Kaynakça
- Almeida, Y., Albuquerque, D., Filho, E. D., Muniz, F., de Farias Santos, K., Perkusich, M., Almeida, H., & Perkusich, A. (2024). AICodeReview: Advancing code quality with AI-enhanced reviews. SoftwareX, 26, 101677. https://doi.org/10.1016/j.softx.2024.101677
- Azeem, M. I., Palomba, F., Shi, L., & Wang, Q. (2019). Machine learning techniques for code smell detection: A systematic literature review and meta-analysis. Information and Software Technology, 108, 115–138. https://doi.org/10.1016/j.infsof.2018.12.009
- Bacchelli, A., & Bird, C. (2013). Expectations, outcomes, and challenges of modern code review. Proceedings of the 35th International Conference on Software Engineering (ICSE) (pp. 712–721). IEEE.
- Ball, T., & Larus, J. R. (1994). Optimally profiling and tracing programs. ACM Transactions on Programming Languages and Systems, 16(4), 1319–1360.
- Banerjee, S., & Lavie, A. (2005). METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization (pp. 65–72). Association for Computational Linguistics.
- Bani-Salameh, H., Sallam, M., & Alshboul, B. (2021). A deep-learning-based bug priority detection using RNN-LSTM neural networks. e-Informatica Software Engineering Journal, 15(1), 29–45.
- Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., ... Amodei, D. (2020). Language models are few-shot learners. Proceedings of the 34th International Conference on Neural Information Processing Systems (NeurIPS) (pp. 1877–1901). Curran Associates Inc.
- Bughin, J. (2024). The role of firm AI capabilities in generative AI-pair coding. Journal of Decision Systems, 1–22. https://doi.org/10.1080/12460125.2024.2428187
Ayrıntılar
Birincil Dil
İngilizce
Konular
Bilgi Sistemleri Geliştirme Metodolojileri ve Uygulamaları, Bilgi Sistemleri (Diğer)
Bölüm
Derleme
Yayımlanma Tarihi
15 Mayıs 2026
Gönderilme Tarihi
8 Şubat 2025
Kabul Tarihi
25 Ocak 2026
Yayımlandığı Sayı
Yıl 2026 Cilt: 9 Sayı: 3
APA
İçöz, B., & Biricik, G. (2026). The Evolution of Code Review: From Traditional Methods to AI-Powered Approaches. Black Sea Journal of Engineering and Science, 9(3), 1478-1491. https://doi.org/10.34248/bsengineering.1635994
AMA
1.İçöz B, Biricik G. The Evolution of Code Review: From Traditional Methods to AI-Powered Approaches. BSJ Eng. Sci. 2026;9(3):1478-1491. doi:10.34248/bsengineering.1635994
Chicago
İçöz, Büşra, ve Göksel Biricik. 2026. “The Evolution of Code Review: From Traditional Methods to AI-Powered Approaches”. Black Sea Journal of Engineering and Science 9 (3): 1478-91. https://doi.org/10.34248/bsengineering.1635994.
EndNote
İçöz B, Biricik G (01 Mayıs 2026) The Evolution of Code Review: From Traditional Methods to AI-Powered Approaches. Black Sea Journal of Engineering and Science 9 3 1478–1491.
IEEE
[1]B. İçöz ve G. Biricik, “The Evolution of Code Review: From Traditional Methods to AI-Powered Approaches”, BSJ Eng. Sci., c. 9, sy 3, ss. 1478–1491, May. 2026, doi: 10.34248/bsengineering.1635994.
ISNAD
İçöz, Büşra - Biricik, Göksel. “The Evolution of Code Review: From Traditional Methods to AI-Powered Approaches”. Black Sea Journal of Engineering and Science 9/3 (01 Mayıs 2026): 1478-1491. https://doi.org/10.34248/bsengineering.1635994.
JAMA
1.İçöz B, Biricik G. The Evolution of Code Review: From Traditional Methods to AI-Powered Approaches. BSJ Eng. Sci. 2026;9:1478–1491.
MLA
İçöz, Büşra, ve Göksel Biricik. “The Evolution of Code Review: From Traditional Methods to AI-Powered Approaches”. Black Sea Journal of Engineering and Science, c. 9, sy 3, Mayıs 2026, ss. 1478-91, doi:10.34248/bsengineering.1635994.
Vancouver
1.Büşra İçöz, Göksel Biricik. The Evolution of Code Review: From Traditional Methods to AI-Powered Approaches. BSJ Eng. Sci. 01 Mayıs 2026;9(3):1478-91. doi:10.34248/bsengineering.1635994