References
Agarwal, S., Miller, C., Kästner, C., & Vasilescu, B. (2026). 3100 opinions on code review in an AI world: Building causal theory from practitioner discourse. arXiv. https://arxiv.org/abs/2607.07980 Bacchelli, A., & Bird, C. (2013). Expectations, outcomes, and challenges of modern code review. In Proceedings of the 2013 International Conference on Software Engineering (pp. 712– 721). IEEE. https://doi.org/10.1109/ICSE.2013.6606617 CodeAnt AI. (2026a, March 12). How to review AI-generated code in 2026: Pipeline, tools, and best practices. https://codeant.ai/blogs/how-to-review-ai-generated-code CodeAnt AI. (2026b, July 1). Code review best practices for developers in 2026. https://www.codeant.ai/blogs/code-review-best-practices Czerwonka, J., Greiler, M., & Tilford, J. (2015). Code reviews do not find bugs: How the current code review best practice slows us down. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (Vol. 2, pp. 27–28). IEEE. Dolcetti, G., & Iotti, E. (2025). A dual perspective review on large language models and code verification. Frontiers in Computer Science, 7, Article 1655469. https://doi.org/10.3389/fcomp.2025.1655469 Exceeds AI. (2026, April 12). 7 AI code review practices that prove ROI in 2026. https://blog.exceeds.ai/ai-code-review-best-practices/ Fagan, M. E. (1976). Design and code inspections to reduce errors in program development. IBM Systems Journal, 15(3), 182–211. https://doi.org/10.1147/sj.153.0182 Guo, H., Zheng, X., Liao, Z., Yu, H., Di, P., Zhang, Z., & Dai, H.-N. (2025). CodeFuse-CR-Bench: A comprehensiveness-aware benchmark for end-to-end code review evaluation in Python projects. arXiv. https://arxiv.org/abs/2509.14856 Haider, M. A., & Zimmermann, T. (2026). Understanding dominant themes in reviewing agentic AI-authored code. arXiv. https://arxiv.org/abs/2601.19287 Islam Khan, T., Wang, S., Zhang, H., & Chen, T.-H. (2026). A survey of code review benchmarks and evaluation practices in pre-LLM and LLM era. arXiv. https://arxiv.org/abs/2602.13377 Jaoua, I., Ben Sghaier, O., & Sahraoui, H. (2025). Combining large language models with static analyzers for code review generation. In 2025 IEEE/ACM 22nd International Conference on Mining Software Repositories (pp. 174–186). IEEE. https://doi.org/10.48550/arXiv.2502.06633 Kamalı, H. Ö., Tuna, E., Haratian, V., & Tüzün, E. (2026). Rethinking code review in the age of AI: A vision for agentic code review. arXiv. https://arxiv.org/abs/2605.17548 McIntosh, S., Kamei, Y., Adams, B., & Hassan, A. E. (2014). The impact of code review coverage and code review participation on software quality: A case study of the Qt, VTK, and ITK projects. In Proceedings of the 11th Working Conference on Mining Software Repositories (pp. 192–201). ACM. https://doi.org/10.1145/2597073.2597076 Metacto. (2026, July 8). AI-generated code review checklist: 2026 standards for teams. https://www.metacto.com/blogs/establishing-code-review-standards-for-ai-generated- code Pensero. (2026, March 6). AI code review for enterprises in 2026. https://pensero.ai/blog/ai-code- review-for-enterprise Pereira, K., Sinha, N., Ghosh, R., & Dutta, D. (2026). CR-Bench: Evaluating the real-world utility of AI code review agents. arXiv. https://doi.org/10.48550/arXiv.2603.11078 IJCSMT Perry, N., Srivastava, M., Kumar, D., & Boneh, D. (2023). Do users write more insecure code with AI assistants? In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (pp. 2785–2799). ACM. https://doi.org/10.1145/3576915.3623157 Rigby, P. C., & Bird, C. (2013). Convergent contemporary software peer review practices. In Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering (pp. 202–212). ACM. https://doi.org/10.1145/2491411.2491444 Samsonau, S. V. (2026). Scicode-lint: Detecting methodology bugs in scientific Python code with LLM-generated patterns. arXiv. https://arxiv.org/abs/2603.17893 Tantithamthavorn, K., Zou, Y., Wong, A., Gupta, M., Wang, Z., Buller, M., Jiang, R., Watson, M., Jeong, M., Chen, K., & Wu, M. (2026). RovoDev code reviewer: A large-scale online evaluation of LLM-based code review automation at Atlassian. In Proceedings of the 48th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP '26). IEEE. https://doi.org/10.48550/arXiv.2601.01129 Tanner, M. (2026, May 21). AI code review in 2026: How it works and how to adopt it. Sourcegraph. https://sourcegraph.com/blog/ai-code-review Zhang, L., Yu, Y., Yu, M., Guo, X., Zhuang, Z., Rong, G., Shao, D., Shen, H., Kuang, H., Li, Z., Wang, B., Zhang, G., Xiang, B., & Xu, X. (2026). AACR-Bench: Evaluating automatic code review with holistic repository-level context. arXiv. https://arxiv.org/abs/2601.19494 Zhang, Y., Pan, Z., Yusuf, I. N. B., Ruan, H., Shariffdeen, R., & Roychoudhury, A. (2026). Code review agent benchmark. arXiv. https://arxiv.org/abs/2603.23448 Zhong, S., Noei, S., Zou, Y., & Adams, B. (2026). Human-AI synergy in agentic code review. arXiv. https://arxiv.org/abs/2603.15911