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CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generation
Bo Xiong, Linghao Zhang, Chong Wang, Peng Liang
https://arxiv.org/abs/2509.18337 https://

CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generation
Commit messages play a key role in documenting the intent behind code changes. However, they are often low-quality, vague, or incomplete, limiting their usefulness. Commit Message Generation (CMG) aims to automatically generate descriptive commit messages from code diffs to reduce developers' effort and improve message quality. Although recent advances in LLMs have shown promise in automating CMG, their performance remains limited. This paper aims to enhance CMG performance by retrieving simila…

Brevity is the Soul of Wit: Condensing Code Changes to Improve Commit Message Generation
Hongyu Kuang, Ning Zhang, Hui Gao, Xin Zhou, Wesley K. G. Assun\c{c}\~ao, Xiaoxing Ma, Dong Shao, Guoping Rong, He Zhang
https://arxiv.org/abs/2509.15567

Brevity is the Soul of Wit: Condensing Code Changes to Improve Commit Message Generation
Commit messages are valuable resources for describing why code changes are committed to repositories in version control systems (e.g., Git). They effectively help developers understand code changes and better perform software maintenance tasks. Unfortunately, developers often neglect to write high-quality commit messages in practice. Therefore, a growing body of work is proposed to generate commit messages automatically. These works all demonstrated that how to organize and represent code chang…

Evaluating Generated Commit Messages with Large Language Models
Qunhong Zeng, Yuxia Zhang, Zexiong Ma, Bo Jiang, Ningyuan Sun, Klaas-Jan Stol, Xingyu Mou, Hui Liu
https://arxiv.org/abs/2507.10906

Evaluating Generated Commit Messages with Large Language Models
Commit messages are essential in software development as they serve to document and explain code changes. Yet, their quality often falls short in practice, with studies showing significant proportions of empty or inadequate messages. While automated commit message generation has advanced significantly, particularly with Large Language Models (LLMs), the evaluation of generated messages remains challenging. Traditional reference-based automatic metrics like BLEU, ROUGE-L, and METEOR have notable…

VulGuard: An Unified Tool for Evaluating Just-In-Time Vulnerability Prediction Models
Duong Nguyen, Manh Tran-Duc, Thanh Le-Cong, Triet Huynh Minh Le, M. Ali Babar, Quyet-Thang Huynh
https://arxiv.org/abs/2507.16685

VulGuard: An Unified Tool for Evaluating Just-In-Time Vulnerability Prediction Models
We present VulGuard, an automated tool designed to streamline the extraction, processing, and analysis of commits from GitHub repositories for Just-In-Time vulnerability prediction (JIT-VP) research. VulGuard automatically mines commit histories, extracts fine-grained code changes, commit messages, and software engineering metrics, and formats them for downstream analysis. In addition, it integrates several state-of-the-art vulnerability prediction models, allowing researchers to train, evaluat…