AI-Powered Commit Explorer (APCE)
Yousab Grees, Polina Iaremchuk, Ramtin Ehsani, Esteban Parra, Preetha Chatterjee, Sonia Haiduc
TL;DR
The paper introduces APCE, a tool that supports the use and evaluation of LLM-generated commit messages to improve software documentation and facilitate research in this area.
Contribution
APCE provides a novel platform for storing prompts, enhancing commit messages, and enabling automated and human evaluation of LLM-generated messages.
Findings
Supports storage of multiple prompts for LLMs
Provides mechanisms for message enhancement and evaluation
Facilitates research on commit message quality
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…
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Taxonomy
TopicsBig Data and Business Intelligence