Automated Commit Message Generation With Large Language Models: An Empirical Study and Beyond

Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">diff</i>s, which facilitate collaboration among developers and play a critical role in Open-Source Software (OSS). Very recently, Large Language Models (LLMs) have been applied in diverse code-related tasks owing to their powerful generality. Yet, in the CMG field, few studies systematically explored their effectiveness. This paper conducts the first comprehensive experiment to investigate how far we have been in applying LLM to generate high-quality commit messages and how to go further beyond in this field. Motivated by a pilot analysis, we first construct a multi-lingual high-quality CMG test set following practitioners’ criteria. Afterward, we re-evaluate diverse CMG approaches and make comparisons with recent LLMs. To delve deeper into LLMs’ ability, we further propose four manual metrics following the practice of OSS, including Accuracy, Integrity, Readability, and Applicability for assessment. Results reveal that LLMs have outperformed existing CMG approaches overall, and different LLMs carry different advantages, where GPT-3.5 performs best. To further boost LLMs’ performance in the CMG task, we propose an Efficient Retrieval-based In-Context Learning (ICL) framework, namely ERICommiter, which leverages a two-step filtering to accelerate the retrieval efficiency and introduces semantic/lexical-based retrieval algorithm to construct the ICL examples, thereby guiding the generation of high-quality commit messages with LLMs. Extensive experiments demonstrate the substantial performance improvement of ERICommiter on various LLMs across different programming languages. Meanwhile, ERICommiter also significantly reduces the retrieval time while keeping almost the same performance. Our research contributes to the understanding of LLMs’ capabilities in the CMG field and provides valuable insights for practitioners seeking to leverage these tools in their workflows.