Commit messages are essential for understanding software evolution and maintaining traceability of projects; however, their quality varies across repositories. Recent Large Language Models provide a promising path to automate this task by generating concise context-sensitive commit messages directly from code diffs. This paper provides a comparative study of three paradigms of large language models: zero-shot prompting, retrieval-augmented generation, and fine-tuning, using the large-scale CommitBench dataset that spans six programming languages. We assess the performance of the models with automatic metrics, namely BLEU, ROUGE-L, METEOR, and Adequacy, and a human assessment of 100 commits. In the latter, experienced developers rated each generated commit message for Adequacy and Fluency on a five-point Likert scale. The results show that fine-tuning and domain adaptation yield models that perform consistently better than general-purpose baselines across all evaluation metrics, thus generating commit messages with higher semantic adequacy and clearer phrasing than zero-shot approaches. The correlation analysis suggests that the Adequacy and BLEU scores are closer to human judgment, while ROUGE-L and METEOR tend to underestimate the quality in cases where the models generate stylistically diverse or paraphrased outputs. Finally, the study outlines a conceptual integration pathway for incorporating such models into software development workflows, emphasizing a human-in-the-loop approach for quality assurance.

Commit messages are essential for understanding software evolution and maintaining traceability of projects; nevertheless, their quality varies across repositories. Recent Large Language Models provide a promising path to automate this task by generating concise context and sensitive commit messages directly from code diffs. This paper provides a comparative study of three paradigms of large language models: zero-shot prompting, retrieval augmented generation, and fine-tuning, using the large scale CommitBench dataset that spans six programming languages. We assess the performance of the models with automatic metrics, namely BLEU, ROUGE-L, METEOR, and Adequacy, and a human assessment of 100 commits. In the latter, experienced developers rated each generated commit message for Adequacy and Fluency on a five-point Likert scale. The results show that fine-tuning and domain adaptation yield models that perform consistently better than general-purpose baselines across all evaluation metrics, thus generating commit messages with higher semantic adequacy and clearer phrasing than zero-shot. The correlation analysis suggests that the Adequacy and BLEU scores are closer to human judgment, while ROUGE-L and METEOR tend to underestimate the quality in cases where the models generate stylistically diverse or paraphrased outputs. Finally, the study outlines a conceptual integration pathway for incorporating such models into software development workflows, emphasizing a human in the loop approach for quality assurance.