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The automation of code review activities, a long-standing pursuit in software engineering, has been primarily addressed by numerous domain-specific pre-trained models.
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Q. Shan, D. Sukhdeo, Q. Huang, S. Rogers, L. Chen, E. Paradis, P. C. Rigby, and N. Nagappan, “Using nudges to accelerate code reviews at scale,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2022, pp. 472–482
2022
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J. Zhang, S. Panthaplackel, P. Nie, J. J. Li, and M. Gligoric, “Coditt5: Pretraining for source code and natural language editing,” in 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–12
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LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning . Zenodo, May 2023. [Online]. Available: https://doi.org/10.5281/zenodo.7991113
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R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto, “Stanford alpaca: An instruction-following llama model,” https://github.com/tatsu-lab/stanford_alpaca, 2023
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