LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLM

• URL: http://arxiv.org/abs/2512.01356v1
• Date: Mon, 01 Dec 2025 07:10:23 GMT
• Title: LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLM
• Authors: Yuxin Zhang, Yuxia Zhang, Zeyu Sun, Yanjie Jiang, Hui Liu,
• Abstract summary: This paper proposes an LLM-based review knowledge-augmented, context-aware framework for code review generation, named LAURA.<n>The framework integrates review retrieval, context augmentation, and systematic guidance to enhance the performance of ChatGPT-4o and DeepSeek v3 in generating code review comments.
• Score: 17.54065758880181
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code review is critical for ensuring software quality and maintainability. With the rapid growth in software scale and complexity, code review has become a bottleneck in the development process because of its time-consuming and knowledge-intensive nature and the shortage of experienced developers willing to review code. Several approaches have been proposed for automatically generating code reviews based on retrieval, neural machine translation, pre-trained models, or large language models (LLMs). These approaches mainly leverage historical code changes and review comments. However, a large amount of crucial information for code review, such as the context of code changes and prior review knowledge, has been overlooked. This paper proposes an LLM-based review knowledge-augmented, context-aware framework for code review generation, named LAURA. The framework integrates review exemplar retrieval, context augmentation, and systematic guidance to enhance the performance of ChatGPT-4o and DeepSeek v3 in generating code review comments. Besides, given the extensive low-quality reviews in existing datasets, we also constructed a high-quality dataset. Experimental results show that for both models, LAURA generates review comments that are either completely correct or at least helpful to developers in 42.2% and 40.4% of cases, respectively, significantly outperforming SOTA baselines. Furthermore, our ablation studies demonstrate that all components of LAURA contribute positively to improving comment quality.

Related papers

• RovoDev Code Reviewer: A Large-Scale Online Evaluation of LLM-based Code Review Automation at Atlassian [6.019311657369222]
  Large Language Models (LLMs)-powered code review automation has the potential to transform seamless code review.<n>This paper aims at answering the practical question: how can we design a review-guided, context-aware, quality-checked code review comment generation without fine-tuning?<n>We present RovoDev Code Reviewer, an enterprise-grade LLM-based code review automation tool designed and deployed at scale within Atlassian's development ecosystem with integration into Atlassian's Bitbucket.
  arXiv  Detail & Related papers  (2026-01-03T09:27:56Z)
• MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks [56.34018316319873]
  We propose MERA Code, a benchmark for evaluating code for the latest code generation LLMs in Russian.<n>This benchmark includes 11 evaluation tasks that span 8 programming languages.<n>We evaluate open LLMs and frontier API models, analyzing their limitations in terms of practical coding tasks in non-English languages.
  arXiv  Detail & Related papers  (2025-07-16T14:31:33Z)
• Turning the Tide: Repository-based Code Reflection [52.13709676656648]
  We introduce LiveRepoReflection, a benchmark for evaluating code understanding and generation in multi-file repository contexts.<n>1,888 rigorously filtered test cases across $6$ programming languages to ensure diversity, correctness, and high difficulty.<n>We also create RepoReflection-Instruct, a large-scale, quality-filtered instruction-tuning dataset derived from diverse sources.
  arXiv  Detail & Related papers  (2025-07-14T02:36:27Z)
• Leveraging Reward Models for Guiding Code Review Comment Generation [13.306560805316103]
  Code review is a crucial component of modern software development, involving the evaluation of code quality, providing feedback on potential issues, and refining the code to address identified problems.<n>Deep learning techniques are able to tackle the generative aspect of code review, by commenting on a given code as a human reviewer would do.<n>In this paper, we introduce CoRAL, a deep learning framework automating review comment generation by exploiting reinforcement learning with a reward mechanism.
  arXiv  Detail & Related papers  (2025-06-04T21:31:38Z)
• VERINA: Benchmarking Verifiable Code Generation [46.582574591358735]
  Large language models (LLMs) are increasingly integrated in software development.<n> Ensuring correctness in LLM-generated code remains challenging.<n>Verifiable code generation offers a promising path to address this limitation.
  arXiv  Detail & Related papers  (2025-05-29T06:12:52Z)
• Prompting and Fine-tuning Large Language Models for Automated Code Review Comment Generation [5.6001617185032595]
  Large language models pretrained on both programming and natural language data tend to perform well in code-oriented tasks. We fine-tune open-source Large language models (LLM) in parameter-efficient, quantized low-rank fashion on consumer-grade hardware to improve review comment generation.
  arXiv  Detail & Related papers  (2024-11-15T12:01:38Z)
• Understanding Code Understandability Improvements in Code Reviews [79.16476505761582]
  We analyzed 2,401 code review comments from Java open-source projects on GitHub. 83.9% of suggestions for improvement were accepted and integrated, with fewer than 1% later reverted.
  arXiv  Detail & Related papers  (2024-10-29T12:21:23Z)
• Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
  We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework. Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
  arXiv  Detail & Related papers  (2024-10-02T09:11:10Z)
• Leveraging Reviewer Experience in Code Review Comment Generation [11.224317228559038]
  We train deep learning models to imitate human reviewers in providing natural language code reviews.<n>The quality of the model generated reviews remain sub-optimal due to the quality of the open-source code review data used in model training.<n>We propose a suite of experience-aware training methods that utilise the reviewers' past authoring and reviewing experiences as signals for review quality.
  arXiv  Detail & Related papers  (2024-09-17T07:52:50Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [92.62952504133926]
  This study evaluated the performance of three leading closed-source LLMs and six popular open-source LLMs on three commonly used benchmarks.<n>We developed a taxonomy of bugs for incorrect codes and analyzed the root cause for common bug types.<n>We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• Automating Patch Set Generation from Code Review Comments Using Large Language Models [2.045040820541428]
  We provide code contexts to five popular Large Language Models (LLMs) We obtain the suggested code-changes (patch sets) derived from real-world code-review comments. The performance of each model is meticulously assessed by comparing their generated patch sets against the historical data of human-generated patch-sets.
  arXiv  Detail & Related papers  (2024-04-10T02:46:08Z)
• InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models [56.723509505549536]
  InfiBench is the first large-scale freeform question-answering (QA) benchmark for code to our knowledge. It comprises 234 carefully selected high-quality Stack Overflow questions that span across 15 programming languages. We conduct a systematic evaluation for over 100 latest code LLMs on InfiBench, leading to a series of novel and insightful findings.
  arXiv  Detail & Related papers  (2024-03-11T02:06:30Z)

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.