# RACE: Retrieval-Augmented Commit Message Generation

arXiv: https://arxiv.org/abs/2203.02700 · HTML: https://mlsanity.com/paper/2203.02700/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in RAG for code, one of 15 directions in Retrieval-augmented generation.
- Builds on 17 of its 44 references that are in the map.
- First posted on arXiv 5 Mar 2022.

## Placed in

- Retrieval-augmented generation / [RAG for code](https://mlsanity.com/rag/rag-for-code/)

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [On the Evaluation of Neural Code Summarization](https://mlsanity.com/paper/2107.07112/) (2021)
- [Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback](https://mlsanity.com/paper/2108.13454/) (2021)
- [Automatically Generating Commit Messages from Diffs using Neural Machine Translation](https://mlsanity.com/paper/1708.09492/) (2017)
- [Retrieve and Refine: Exemplar-based Neural Comment Generation](https://mlsanity.com/paper/1910.10419/) (2019)
- [CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees](https://mlsanity.com/paper/2108.12987/) (2021)
- [Learning to Update Natural Language Comments Based on Code Changes](https://mlsanity.com/paper/2004.12169/) (2020)
- [ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking](https://mlsanity.com/paper/1912.02972/) (2019)
- [A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes](https://mlsanity.com/paper/1704.04856/) (2017)
- [On the Evaluation of Commit Message Generation Models: An Experimental Study](https://mlsanity.com/paper/2107.05373/) (2021)
- [CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model](https://mlsanity.com/paper/2105.14242/) (2021)
- [CoreGen: Contextualized Code Representation Learning for Commit Message Generation](https://mlsanity.com/paper/2007.06934/) (2020)
- [Untangling Fine-Grained Code Changes](https://mlsanity.com/paper/1502.06757/) (2015)

## Built on by (most cited first, 27 of 27)

- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [Retrieving Multimodal Information for Augmented Generation: A Survey](https://mlsanity.com/paper/2303.10868/) (2023)
- [Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG](https://mlsanity.com/paper/2406.11147/) (2024)
- [Agents in Software Engineering: Survey, Landscape, and Vision](https://mlsanity.com/paper/2409.09030/) (2024)
- [Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.08826/) (2025)
- [RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing](https://mlsanity.com/paper/2404.19543/) (2024)
- [CoCoSoDa: Effective Contrastive Learning for Code Search](https://mlsanity.com/paper/2204.03293/) (2022)
- [A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation](https://mlsanity.com/paper/2410.19265/) (2024)
- [From Commit Message Generation to History-Aware Commit Message Completion](https://mlsanity.com/paper/2308.07655/) (2023)
- [KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation](https://mlsanity.com/paper/2401.08376/) (2024)
- [SoTaNa: The Open-Source Software Development Assistant](https://mlsanity.com/paper/2308.13416/) (2023)
- [RAIDX: A Retrieval-Augmented Generation and GRPO Reinforcement Learning Framework for Explainable Deepfake Detection](https://mlsanity.com/paper/2508.04524/) (2025)
- [AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion](https://mlsanity.com/paper/2601.19697/) (2026)
- [A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation](https://mlsanity.com/paper/2410.08801/) (2024)
- [ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion](https://mlsanity.com/paper/2401.02993/) (2024)
- [Impact-driven Context Filtering For Cross-file Code Completion](https://mlsanity.com/paper/2508.05970/) (2025)
- [SecureReviewer: Enhancing Large Language Models for Secure Code Review through Secure-aware Fine-tuning](https://mlsanity.com/paper/2510.26457/) (2025)
- [COMET: Generating Commit Messages using Delta Graph Context Representation](https://mlsanity.com/paper/2402.01841/) (2024)
- [Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requests](https://mlsanity.com/paper/2601.17627/) (2026)
- [DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning](https://mlsanity.com/paper/2601.20615/) (2026)
- [PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents](https://mlsanity.com/paper/2607.18859/) (2026)
- [CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detection](https://mlsanity.com/paper/2511.19875/) (2025)
- [Political Events using RAG with LLMs](https://mlsanity.com/paper/2502.15701/) (2025)
- [CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generation](https://mlsanity.com/paper/2509.18337/) (2025)
- [RAG-DIVE: A Dynamic Approach for Multi-Turn Dialogue Evaluation in Retrieval-Augmented Generation](https://mlsanity.com/paper/2604.16310/) (2026)
- [Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation](https://mlsanity.com/paper/2609.01601/) (2026)
- [Efficient SWE Agent Benchmarking via Trajectory-Aware Evaluation](https://mlsanity.com/paper/2609.01603/) (2026)

## Abstract (arXiv)

Commit messages are important for software development and maintenance. Many neural network-based approaches have been proposed and shown promising results on automatic commit message generation. However, the generated commit messages could be repetitive or redundant. In this paper, we propose RACE, a new retrieval-augmented neural commit message generation method, which treats the retrieved similar commit as an exemplar and leverages it to generate an accurate commit message. As the retrieved commit message may not always accurately describe the content/intent of the current code diff, we also propose an exemplar guider, which learns the semantic similarity between the retrieved and current code diff and then guides the generation of commit message based on the similarity. We conduct extensive experiments on a large public dataset with five programming languages. Experimental results show that RACE can outperform all baselines. Furthermore, RACE can boost the performance of existing Seq2Seq models in commit message generation.