# Contextual Code Retrieval for Commit Message Generation: A Preliminary Study

arXiv: https://arxiv.org/abs/2507.17690 · HTML: https://mlsanity.com/paper/2507.17690/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2025.

## Built on by (most cited first, 3 of 3)

- [Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requests](https://mlsanity.com/paper/2601.17627/) (2026)
- [CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detection](https://mlsanity.com/paper/2511.19875/) (2025)
- [CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generation](https://mlsanity.com/paper/2509.18337/) (2025)

## Abstract (arXiv)

A commit message describes the main code changes in a commit and plays a crucial role in software maintenance. Existing commit message generation (CMG) approaches typically frame it as a direct mapping which inputs a code diff and produces a brief descriptive sentence as output. However, we argue that relying solely on the code diff is insufficient, as raw code diff fails to capture the full context needed for generating high-quality and informative commit messages. In this paper, we propose a contextual code retrieval-based method called C3Gen to enhance CMG by retrieving commit-relevant code snippets from the repository and incorporating them into the model input to provide richer contextual information at the repository scope. In the experiments, we evaluated the effectiveness of C3Gen across various models using four objective and three subjective metrics. Meanwhile, we design and conduct a human evaluation to investigate how C3Gen-generated commit messages are perceived by human developers. The results show that by incorporating contextual code into the input, C3Gen enables models to effectively leverage additional information to generate more comprehensive and informative commit messages with greater practical value in real-world development scenarios. Further analysis underscores concerns about the reliability of similaritybased metrics and provides empirical insights for CMG.