# AI-Powered Commit Explorer (APCE)

arXiv: https://arxiv.org/abs/2507.16063 · HTML: https://mlsanity.com/paper/2507.16063/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking](https://mlsanity.com/paper/1912.02972/) (2019)
- [What Makes a Good Commit Message?](https://mlsanity.com/paper/2202.02974/) (2022)
- [Using Large Language Models for Commit Message Generation: A Preliminary Study](https://mlsanity.com/paper/2401.05926/) (2024)
- [Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond](https://mlsanity.com/paper/2404.14824/) (2024)
- [CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back](https://mlsanity.com/paper/2302.03924/) (2023)

## Abstract (arXiv)

Commit messages in a version control system provide valuable information for developers regarding code changes in software systems. Commit messages can be the only source of information left for future developers describing what was changed and why. However, writing high-quality commit messages is often neglected in practice. Large Language Model (LLM) generated commit messages have emerged as a way to mitigate this issue. We introduce the AI-Powered Commit Explorer (APCE), a tool to support developers and researchers in the use and study of LLM-generated commit messages. APCE gives researchers the option to store different prompts for LLMs and provides an additional evaluation prompt that can further enhance the commit message provided by LLMs. APCE also provides researchers with a straightforward mechanism for automated and human evaluation of LLM-generated messages. Demo link https://youtu.be/zYrJ9s6sZvo