Code Review Automation using Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2511.05302v1
• Date: Fri, 07 Nov 2025 15:02:42 GMT
• Title: Code Review Automation using Retrieval Augmented Generation
• Authors: Qianru Meng, Xiao Zhang, Zhaochen Ren, Joost Visser,
• Abstract summary: Code review is essential for maintaining software quality but is labor-intensive.<n> deep learning-based generative techniques and retrieval-based methods have demonstrated strong performance in this task.<n>We introduce Retrieval-Augmented Reviewer (RARe), which combines retrieval-based and generative methods.
• Score: 3.438467395627969
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code review is essential for maintaining software quality but is labor-intensive. Automated code review generation offers a promising solution to this challenge. Both deep learning-based generative techniques and retrieval-based methods have demonstrated strong performance in this task. However, despite these advancements, there are still some limitations where generated reviews can be either off-point or overly general. To address these issues, we introduce Retrieval-Augmented Reviewer (RARe), which leverages Retrieval-Augmented Generation (RAG) to combine retrieval-based and generative methods, explicitly incorporating external domain knowledge into the code review process. RARe uses a dense retriever to select the most relevant reviews from the codebase, which then enrich the input for a neural generator, utilizing the contextual learning capacity of large language models (LLMs), to produce the final review. RARe outperforms state-of-the-art methods on two benchmark datasets, achieving BLEU-4 scores of 12.32 and 12.96, respectively. Its effectiveness is further validated through a detailed human evaluation and a case study using an interpretability tool, demonstrating its practical utility and reliability.

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