Cover Image

CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xiong, Bo, Zhang, Linghao, Ren, Zongen, Wang, Chong, Liang, Peng
Format:	Preprint
Published:	2025
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Contextual Code Retrieval for Commit Message Generation: A Preliminary Study
by: Xiong, Bo, et al.
Published: (2025)

RAG-Enhanced Commit Message Generation
by: Zhang, Linghao, et al.
Published: (2024)

Using Large Language Models for Commit Message Generation: A Preliminary Study
by: Zhang, Linghao, et al.
Published: (2024)

Evaluating Generated Commit Messages with Large Language Models
by: Zeng, Qunhong, et al.
Published: (2025)

CommitSuite: A Comprehensive Benchmark for Commit Classification and Message Generation
by: Wan, Zirui, et al.
Published: (2026)

Optimization is Better than Generation: Optimizing Commit Message Leveraging Human-written Commit Message
by: Li, Jiawei, et al.
Published: (2025)

CoMRAT: Commit Message Rationale Analysis Tool
by: Dhaouadi, Mouna, et al.
Published: (2025)

Automated Generation of Commit Messages in Software Repositories
by: Palakodeti, Varun Kumar, et al.
Published: (2025)

CommitBench: A Benchmark for Commit Message Generation
by: Schall, Maximilian, et al.
Published: (2024)

KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation
by: Tao, Wei, et al.
Published: (2024)

Brevity is the Soul of Wit: Condensing Code Changes to Improve Commit Message Generation
by: Kuang, Hongyu, et al.
Published: (2025)

An Empirical Study on Commit Message Generation using LLMs via In-Context Learning
by: Wu, Yifan, et al.
Published: (2025)

Context Conquers Parameters: Outperforming Proprietary LLM in Commit Message Generation
by: Imani, Aaron, et al.
Published: (2024)

Commit Messages in the Age of Large Language Models
by: Lopes, Cristina V., et al.
Published: (2024)

Improving the Quality of Commit Messages in Students' Projects
by: Ma, Iris, et al.
Published: (2023)

When More Retrieval Hurts: Retrieval-Augmented Code Review Generation
by: Meng, Qianru, et al.
Published: (2025)

FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion
by: Guo, Qi, et al.
Published: (2024)

CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detection
by: Zhang, Qingyu, et al.
Published: (2025)

Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond
by: Xue, Pengyu, et al.
Published: (2024)

Preference-Guided Refactored Tuning for Retrieval Augmented Code Generation
by: Gao, Xinyu, et al.
Published: (2024)

On the Informativeness of Security Commit Messages: A Large-scale Replication Study
by: Islam, Syful, et al.
Published: (2026)

Rationale Dataset and Analysis for the Commit Messages of the Linux Kernel Out-of-Memory Killer
by: Dhaouadi, Mouna, et al.
Published: (2024)

On the Use of Commit Messages for Corrective Software Maintenance: A Systematic Mapping Study
by: Islam, Syful, et al.
Published: (2026)

Exploring the Security Threats of Retriever Backdoors in Retrieval-Augmented Code Generation
by: Li, Tian, et al.
Published: (2025)

Help Me to Understand this Commit! -- A Vision for Contextualized Code Reviews
by: Unterkalmsteiner, Michael, et al.
Published: (2024)

Consider What Humans Consider: Optimizing Commit Message Leveraging Contexts Considered By Human
by: Li, Jiawei, et al.
Published: (2025)

Contextualized Code Pretraining for Code Generation
by: Liu, Chen, et al.
Published: (2026)

DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning
by: Wang, Yanlin, et al.
Published: (2026)

Improving Retrieval-Augmented Code Comment Generation by Retrieving for Generation
by: Lu, Hanzhen, et al.
Published: (2024)

A Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChat
by: Yang, Zezhou, et al.
Published: (2025)

COMET: Generating Commit Messages using Delta Graph Context Representation
by: Mandli, Abhinav Reddy, et al.
Published: (2024)

RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines
by: Xu, Xiwei, et al.
Published: (2025)

Back to the Basics: Rethinking Issue-Commit Linking with LLM-Assisted Retrieval
by: Huang, Huihui, et al.
Published: (2025)

What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond
by: Gu, Wenchao, et al.
Published: (2025)

Retrieval-Augmented Code Review Comment Generation
by: Hong, Hyunsun, et al.
Published: (2025)

Enhancing LLM-based Fault Localization with a Functionality-Aware Retrieval-Augmented Generation Framework
by: Shi, Xinyu, et al.
Published: (2025)

Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generation
by: Lin, Bo, et al.
Published: (2025)

ReVul-CoT: Towards Effective Software Vulnerability Assessment with Retrieval-Augmented Generation and Chain-of-Thought Prompting
by: Chen, Zhijie, et al.
Published: (2025)

TIGER: A Generating-Then-Ranking Framework for Practical Python Type Inference
by: Wang, Chong, et al.
Published: (2024)

Assessing UML Diagrams by GPT: Implications for Education
by: Wang, Chong, et al.
Published: (2024)