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The Evolution of Code Review: From Traditional Methods to AI-Powered Approaches

Cilt: 9 Sayı: 3 15 Mayıs 2026
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The Evolution of Code Review: From Traditional Methods to AI-Powered Approaches

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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

  1. 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
  2. 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
  3. 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.
  4. Ball, T., & Larus, J. R. (1994). Optimally profiling and tracing programs. ACM Transactions on Programming Languages and Systems, 16(4), 1319–1360.
  5. 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.
  6. 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.
  7. 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.
  8. 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

Kaynak Göster

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

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