# Issue-Oriented Agent-Based Framework for Automated Review Comment Generation

arXiv: https://arxiv.org/abs/2511.00517 · HTML: https://mlsanity.com/paper/2511.00517/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 40 of its 66 references that are in the map.
- First posted on arXiv 1 Nov 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 30 of 40)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [Large Language Models as Tool Makers](https://mlsanity.com/paper/2305.17126/) (2023)
- [CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges](https://mlsanity.com/paper/2401.07339/) (2024)
- [MapCoder: Multi-Agent Code Generation for Competitive Problem Solving](https://mlsanity.com/paper/2405.11403/) (2024)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [Few-shot training LLMs for project-specific code-summarization](https://mlsanity.com/paper/2207.04237/) (2022)
- [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)
- [Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning](https://mlsanity.com/paper/2304.11384/) (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)
- [Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models](https://mlsanity.com/paper/2304.13835/) (2023)
- [Bidirectional Language Models Are Also Few-shot Learners](https://mlsanity.com/paper/2209.14500/) (2022)
- [Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study](https://mlsanity.com/paper/2309.08221/) (2023)
- [CodeAgent: Autonomous Communicative Agents for Code Review](https://mlsanity.com/paper/2402.02172/) (2024)
- [Chain-of-Thought in Neural Code Generation: From and For Lightweight Language Models](https://mlsanity.com/paper/2312.05562/) (2023)
- [AUGER: Automatically Generating Review Comments with Pre-training Models](https://mlsanity.com/paper/2208.08014/) (2022)
- [BitsAI-CR: Automated Code Review via LLM in Practice](https://mlsanity.com/paper/2501.15134/) (2025)
- [Code Review Automation: Strengths and Weaknesses of the State of the Art](https://mlsanity.com/paper/2401.05136/) (2024)
- [Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation](https://mlsanity.com/paper/2404.16045/) (2024)
- [Developer-Intent Driven Code Comment Generation](https://mlsanity.com/paper/2302.07055/) (2023)
- [Balancing Cost and Effectiveness of Synthetic Data Generation Strategies for LLMs](https://mlsanity.com/paper/2409.19759/) (2024)
- [Semantic Similarity Metrics for Evaluating Source Code Summarization](https://mlsanity.com/paper/2204.01632/) (2022)
- [DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation](https://mlsanity.com/paper/2412.18291/) (2024)

## Built on by (most cited first, 4 of 4)

- [AACR-Bench: Evaluating Automatic Code Review with Holistic Repository-Level Context](https://mlsanity.com/paper/2601.19494/) (2026)
- [Code Review Agent Benchmark](https://mlsanity.com/paper/2603.23448/) (2026)
- [ConceptRM: The Quest to Mitigate Alert Fatigue through Consensus-Based Purity-Driven Data Cleaning for Reflection Modelling](https://mlsanity.com/paper/2602.20166/) (2026)
- [From Static to Dynamic: Benchmarking Real-World Code Review with MCR-Bench](https://mlsanity.com/paper/2608.27442/) (2026)

## Abstract (arXiv)

Code review (CR) is a crucial practice for ensuring software quality. Various automated review comment generation techniques have been proposed to streamline the labor-intensive process. However, existing approaches heavily rely on a single model to identify various issues within the code, limiting the model's ability to handle the diverse, issue-specific nature of code changes and leading to non-informative comments, especially in complex scenarios such as bug fixes. To address these limitations, we propose RevAgent, a novel agent-based issue-oriented framework, decomposes the task into three stages: (1) Generation Stage, where five category-specific commentator agents analyze code changes from distinct issue perspectives and generate candidate comments; (2) Discrimination Stage, where a critic agent selects the most appropriate issue-comment pair; and (3) Training Stage, where all agents are fine-tuned on curated, category-specific data to enhance task specialization. Evaluation results show that RevAgent significantly outperforms state-of-the-art PLM- and LLM-based baselines, with improvements of 12.90%, 10.87%, 6.32%, and 8.57% on BLEU, ROUGE-L, METEOR, and SBERT, respectively. It also achieves relatively higher accuracy in issue-category identification, particularly for challenging scenarios. Human evaluations further validate the practicality of RevAgent in generating accurate, readable, and context-aware review comments. Moreover, RevAgent delivers a favorable trade-off between performance and efficiency.