RAG-Enhanced Commit Message Generation

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, 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.