  Commit message is one of the most important textual information in software
development and maintenance. However, it is time-consuming and labor-intensive
to write commit messages manually. Commit Message Generation (CMG) has become a
research hotspot in automated software engineering. Researchers have proposed
several methods for CMG and achieved great results. In recent years, CodeBERT,
CodeT5, and other Pre-trained Language Models (PLMs) for code have been
proposed. These models can be easily transferred to code-related downstream
tasks including CMG with simple fine-tuning and can achieve impressive
performance. Moreover, Large Language Models (LLMs) with code capabilities
(e.g., ChatGPT, Llama 3, Gemma) can be directly applied to various tasks by
designing instruct prompts without training. This brings new possibilities to
the CMG task. In this work, we propose REACT, a novel REtrieval-Augmented
framework for CommiT message generation, which effectively integrates advanced
retrieval techniques with different PLMs and LLMs and can broadly enhance the
performance of various models on the CMG task. Specifically, we design and
build a hybrid retriever to retrieve the most relevant code diff and commit
message pair from the code base as an "exemplar". Then, the retrieved pair is
utilized to guide and enhance the generation of commit messages by PLMs and
LLMs through fine-tuning and in-context learning. Our approach is evaluated on
a widely-used dataset. The experimental results show that REACT significantly
enhances the performance of various models on the CMG task, improving the BLEU
score of CodeT5 by up to 55%, boosting Llama 3's BLEU score by 102%, and
substantially surpassing all baselines, achieving a new SOTA. This demonstrates
the effectiveness and broad applicability of our framework that can enhance CMG
by a large margin.
