AI-Powered Commit Explorer (APCE)

2025/07/21 by Yousab Grees, Polina Iaremchuk, Grees, Yousab +9
Business, Management and Accounting · #Artificial Intelligence (cs.AI) #Big Data and Business Intelligence #FOS: Computer and information sciences #Software Engineering (cs.SE)

paper · pdf · doi:10.48550/arxiv.2507.16063

openalex publication_date 2025/07/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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

Citations

• Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond
• LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead
• LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision, and the Road Ahead
• Automatic Commit Message Generation: A Critical Review and Directions for Future Work
• Using Large Language Models for Commit Message Generation: A Preliminary Study
• A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
• CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back
• ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking

Related

• Using Large Language Models for Commit Message Generation: A Preliminary Study
• Commit Messages in the Age of Large Language Models
• Evaluating Generated Commit Messages with Large Language Models
• Context Conquers Parameters: Outperforming Proprietary LLM in Commit Message Generation
• Consider What Humans Consider: Optimizing Commit Message Leveraging Contexts Considered By Human