# LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLM arXiv: https://arxiv.org/abs/2512.01356 ยท HTML: https://mlsanity.com/paper/2512.01356/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Placed in RAG for code, one of 15 directions in Retrieval-augmented generation. - Builds on 29 of its 70 references that are in the map. - First posted on arXiv 1 Dec 2025. ## Placed in - Retrieval-augmented generation / [RAG for code](https://mlsanity.com/rag/rag-for-code/) ## Builds on (most cited first, 29 of 29) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023) - [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022) - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021) - [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023) - [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021) - [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023) - [CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://mlsanity.com/paper/2305.07922/) (2023) - [Automating Code Review Activities by Large-Scale Pre-training](https://mlsanity.com/paper/2203.09095/) (2022) - [Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)](https://mlsanity.com/paper/2304.06815/) (2023) - [ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design](https://mlsanity.com/paper/2303.07839/) (2023) - [GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis](https://mlsanity.com/paper/2308.03314/) (2023) - [Using Pre-Trained Models to Boost Code Review Automation](https://mlsanity.com/paper/2201.06850/) (2022) - [Towards Automating Code Review Activities](https://mlsanity.com/paper/2101.02518/) (2021) - [LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2308.11148/) (2023) - [Prompt Design and Engineering: Introduction and Advanced Methods](https://mlsanity.com/paper/2401.14423/) (2024) - [Large Language Model for Vulnerability Detection: Emerging Results and Future Directions](https://mlsanity.com/paper/2401.15468/) (2024) - [Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study](https://mlsanity.com/paper/2309.08221/) (2023) - [Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation](https://mlsanity.com/paper/2402.00905/) (2024) - [What Makes a Good Commit Message?](https://mlsanity.com/paper/2202.02974/) (2022) - [Code Review Automation: Strengths and Weaknesses of the State of the Art](https://mlsanity.com/paper/2401.05136/) (2024) - [From Commit Message Generation to History-Aware Commit Message Completion](https://mlsanity.com/paper/2308.07655/) (2023) - [DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation](https://mlsanity.com/paper/2412.18291/) (2024) - [A ground-truth dataset and classification model for detecting bots in GitHub issue and PR comments](https://mlsanity.com/paper/2010.03303/) (2020) - [What Makes a Code Review Useful to OpenDev Developers? An Empirical Investigation](https://mlsanity.com/paper/2302.11686/) (2023) - [Recommending Stack Overflow Posts for Fixing Runtime Exceptions using Failure Scenario Matching](https://mlsanity.com/paper/2009.10174/) (2020) - [CommitBench: A Benchmark for Commit Message Generation](https://mlsanity.com/paper/2403.05188/) (2024) - [RAG-Enhanced Commit Message Generation](https://mlsanity.com/paper/2406.05514/) (2024) ## Built on by (most cited first, 8 of 8) - [AACR-Bench: Evaluating Automatic Code Review with Holistic Repository-Level Context](https://mlsanity.com/paper/2601.19494/) (2026) - [AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection](https://mlsanity.com/paper/2601.19138/) (2026) - [SWE-PRBench: Benchmarking AI Code Review Quality Against Pull Request Feedback](https://mlsanity.com/paper/2603.26130/) (2026) - [Needle in the Repo: A Benchmark for Maintainability in AI-Generated Repository Edits](https://mlsanity.com/paper/2603.27745/) (2026) - [Understanding the Limits of Automated Evaluation for Code Review Bots in Practice](https://mlsanity.com/paper/2604.24525/) (2026) - [Code Broker: A Multi-Agent System for Automated Code Quality Assessment](https://mlsanity.com/paper/2604.23088/) (2026) - [Archer: Towards Agentic Review for Compiler Optimizations](https://mlsanity.com/paper/2607.01808/) (2026) - [OpenCodeReview: Determinism over Non-Determinism for Cost-Effective Agent-Based Code Review](https://mlsanity.com/paper/2608.09290/) (2026) ## Abstract (arXiv) 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.