用AI工具提升代码提交信息质量，支持自动与人工评估。
AI-Powered Commit Explorer (APCE)
- 提供多提示模板管理，优化LLM生成提交信息
- 内置评估提示，可进一步改进生成结果
- 支持自动化与人工评估，适合研究者使用
版本控制系统中的提交信息为开发者提供了关于代码变更的重要信息，往往是未来开发者了解修改内容和原因的唯一依据。然而，实践中高质量提交信息常被忽视。大语言模型（LLM）生成的提交信息成为缓解此问题的新方式。我们提出AI-powered Commit Explorer（APCE），一个支持开发者与研究者使用和研究LLM生成提交信息的工具。APCE允许研究人员存储不同提示模板，并提供额外的评估提示以进一步优化生成的提交信息。同时，该工具还提供自动化与人工评估机制，便于研究分析。演示链接：https://youtu.be/zYrJ9s6sZvo
原文摘要 · Abstract (English)
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
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