Consider What Humans Consider: Optimizing Commit Message Leveraging Contexts Considered By Human
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913742031159296 |
|---|---|
| author | Li, Jiawei Faragó, David Petrov, Christian Ahmed, Iftekhar |
| author_facet | Li, Jiawei Faragó, David Petrov, Christian Ahmed, Iftekhar |
| contents | Commit messages are crucial in software development, supporting maintenance tasks and communication among developers. While Large Language Models (LLMs) have advanced Commit Message Generation (CMG) using various software contexts, some contexts developers consider to write high-quality commit messages are often missed by CMG techniques and can't be easily retrieved or even retrieved at all by automated tools. To address this, we propose Commit Message Optimization (CMO), which enhances human-written messages by leveraging LLMs and search-based optimization. CMO starts with human-written messages and iteratively improves them by integrating key contexts and feedback from external evaluators. Our extensive evaluation shows CMO generates commit messages that are significantly more Rational, Comprehensive, and Expressive while outperforming state-of-the-art CMG methods and human messages 40.3% to 78.4% of the time. Moreover, CMO can support existing CMG techniques to further improve message quality and generate high-quality messages when the human-written ones are left blank. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11960 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Consider What Humans Consider: Optimizing Commit Message Leveraging Contexts Considered By Human Li, Jiawei Faragó, David Petrov, Christian Ahmed, Iftekhar Software Engineering Commit messages are crucial in software development, supporting maintenance tasks and communication among developers. While Large Language Models (LLMs) have advanced Commit Message Generation (CMG) using various software contexts, some contexts developers consider to write high-quality commit messages are often missed by CMG techniques and can't be easily retrieved or even retrieved at all by automated tools. To address this, we propose Commit Message Optimization (CMO), which enhances human-written messages by leveraging LLMs and search-based optimization. CMO starts with human-written messages and iteratively improves them by integrating key contexts and feedback from external evaluators. Our extensive evaluation shows CMO generates commit messages that are significantly more Rational, Comprehensive, and Expressive while outperforming state-of-the-art CMG methods and human messages 40.3% to 78.4% of the time. Moreover, CMO can support existing CMG techniques to further improve message quality and generate high-quality messages when the human-written ones are left blank. |
| title | Consider What Humans Consider: Optimizing Commit Message Leveraging Contexts Considered By Human |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.11960 |