Retrieval-Augmented Code Review Comment Generation

• URL: http://arxiv.org/abs/2506.11591v1
• Date: Fri, 13 Jun 2025 08:58:20 GMT
• Title: Retrieval-Augmented Code Review Comment Generation
• Authors: Hyunsun Hong, Jongmoon Baik,
• Abstract summary: Automated code review comment generation (RCG) aims to assist developers by automatically producing natural language feedback for code changes.<n>Existing approaches are primarily either generation-based, using pretrained language models, or information retrieval-based (IR), reusing comments from similar past examples.<n>This work proposes to leverage retrieval-augmented generation (RAG) for RCG by conditioning pretrained language models on retrieved code-review exemplars.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated code review comment generation (RCG) aims to assist developers by automatically producing natural language feedback for code changes. Existing approaches are primarily either generation-based, using pretrained language models, or information retrieval-based (IR), reusing comments from similar past examples. While generation-based methods leverage code-specific pretraining on large code-natural language corpora to learn semantic relationships between code and natural language, they often struggle to generate low-frequency but semantically important tokens due to their probabilistic nature. In contrast, IR-based methods excel at recovering such rare tokens by copying from existing examples but lack flexibility in adapting to new code contexts-for example, when input code contains identifiers or structures not found in the retrieval database. To bridge the gap between generation-based and IR-based methods, this work proposes to leverage retrieval-augmented generation (RAG) for RCG by conditioning pretrained language models on retrieved code-review exemplars. By providing relevant examples that illustrate how similar code has been previously reviewed, the model is better guided to generate accurate review comments. Our evaluation on the Tufano et al. benchmark shows that RAG-based RCG outperforms both generation-based and IR-based RCG. It achieves up to +1.67% higher exact match and +4.25% higher BLEU scores compared to generation-based RCG. It also improves the generation of low-frequency ground-truth tokens by up to 24.01%. We additionally find that performance improves as the number of retrieved exemplars increases.

Related papers

• When Retriever Meets Generator: A Joint Model for Code Comment Generation [3.6781644685120924]
  RAGSum is built on top offuse retrieval and generation using a single CodeT5 backbone.<n>A contrastive pre-training phase shapes code embeddings for nearest-neighbor search.<n>A lightweight self-refinement loop is deployed to polish the final output.
  arXiv  Detail & Related papers  (2025-07-16T18:12:27Z)
• Improving Retrieval-Augmented Code Comment Generation by Retrieving for Generation [3.123049150077741]
  We propose a novel training strategy to enable the retriever to learn from the feedback of the generator and retrieve exemplars for generation. By aligning high-score exemplars retrieved by the retriever with low-loss exemplars observed by the generator, the retriever can learn to retrieve exemplars that can best improve the quality of the generated comments.
  arXiv  Detail & Related papers  (2024-08-07T08:32:55Z)
• X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation [21.425178466284017]
  Radiology Report Generation (RRG) has advanced considerably with the development of multimodal generative models.<n>RRG with high performance on existing lexical-based metrics might be more of a mirage - a model can get a high BLEU only by learning the template of reports.<n>We propose a semantics-based evaluation method, which is effective in mitigating the inflated numbers of BLEU and provides more robust evaluation.
  arXiv  Detail & Related papers  (2024-06-25T19:52:01Z)
• CodeRAG-Bench: Can Retrieval Augment Code Generation? [78.37076502395699]
  We conduct a systematic, large-scale analysis of code generation using retrieval-augmented generation.<n>We first curate a comprehensive evaluation benchmark, CodeRAG-Bench, encompassing three categories of code generation tasks.<n>We examine top-performing models on CodeRAG-Bench by providing contexts retrieved from one or multiple sources.
  arXiv  Detail & Related papers  (2024-06-20T16:59:52Z)
• Distillation Enhanced Generative Retrieval [96.69326099136289]
  Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. In this work, we identify a viable direction to further enhance generative retrieval via distillation and propose a feasible framework, named DGR. We conduct experiments on four public datasets, and the results indicate that DGR achieves state-of-the-art performance among the generative retrieval methods.
  arXiv  Detail & Related papers  (2024-02-16T15:48:24Z)
• Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search [7.822427053078387]
  Generation-Augmented Retrieval (GAR) framework generates exemplar code snippets to augment queries. We propose a simple yet effective method that additionally Rewrites the Code (ReCo) within the for style normalization. Code Style Similarity is the first metric tailored to quantify stylistic similarities in code.
  arXiv  Detail & Related papers  (2024-01-09T12:12:50Z)
• Learning to Rank in Generative Retrieval [62.91492903161522]
  Generative retrieval aims to generate identifier strings of relevant passages as the retrieval target. We propose a learning-to-rank framework for generative retrieval, dubbed LTRGR. This framework only requires an additional learning-to-rank training phase to enhance current generative retrieval systems.
  arXiv  Detail & Related papers  (2023-06-27T05:48:14Z)
• Stochastic Code Generation [1.7205106391379026]
  Large language models pre-trained for code generation can generate high-quality short code but often struggle with generating coherent long code. This issue is also observed in language modeling for long text generation. In this study, we investigate whether this technique can be applied to code generation to improve coherence.
  arXiv  Detail & Related papers  (2023-04-14T00:01:05Z)
• ReCode: Robustness Evaluation of Code Generation Models [90.10436771217243]
  We propose ReCode, a comprehensive robustness evaluation benchmark for code generation models. We customize over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format. With human annotators, we verified that over 90% of the perturbed prompts do not alter the semantic meaning of the original prompt.
  arXiv  Detail & Related papers  (2022-12-20T14:11:31Z)
• CodeExp: Explanatory Code Document Generation [94.43677536210465]
  Existing code-to-text generation models produce only high-level summaries of code. We conduct a human study to identify the criteria for high-quality explanatory docstring for code. We present a multi-stage fine-tuning strategy and baseline models for the task.
  arXiv  Detail & Related papers  (2022-11-25T18:05:44Z)
• Enhancing Semantic Code Search with Multimodal Contrastive Learning and Soft Data Augmentation [50.14232079160476]
  We propose a new approach with multimodal contrastive learning and soft data augmentation for code search. We conduct extensive experiments to evaluate the effectiveness of our approach on a large-scale dataset with six programming languages.
  arXiv  Detail & Related papers  (2022-04-07T08:49:27Z)
• Retrieve and Refine: Exemplar-based Neural Comment Generation [27.90756259321855]
  Comments of similar code snippets are helpful for comment generation. We design a novel seq2seq neural network that takes the given code, its AST, its similar code, and its exemplar as input. We evaluate our approach on a large-scale Java corpus, which contains about 2M samples.
  arXiv  Detail & Related papers  (2020-10-09T09:33:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.