Conventional Commit Classification using Large Language Models and Prompt Engineering
Conventional commits provide a structured format for writing commit messages, which improves readability, software maintenance, and enables automation tools such as changelog generators and semantic versioning systems. Existing approaches to conventional commit classification typically rely on ML/DL models trained on large labeled datasets. In this paper, we investigated a training-free alternative by leveraging large language models (LLMs) through prompt engineering. Rather than building a task-specific classifier, we evaluate three prompting strategies, such as zero-shot, few-shot, and chain-of-thought, across three open-source LLMs of varying scale: Mistral-7B-Instruct, LLaMA-3-8B, and...
| Authors | H. M. Sazzad Quadir, Sakib Al Hasan, Md. Nurul Ahad Tawhid |
|---|---|
| Primary category | cs.SE |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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