GitHub commits, which record the code changes with natural language messages for description, play a critical role for software developers to comprehend the software evolution. To promote the development of the open-source software community, we collect a commit benchmark including over 7.99 million commits across 7 programming languages. Based on this benchmark, we present CommitBART, a large pre-trained encoder-decoder Transformer model for GitHub commits. The model is pre-trained by three categories (i.e., denoising objectives, cross-modal generation and contrastive learning) for six pre-training tasks to learn commit fragment representations. Furthermore, we unify a ``commit intelligence'' framework with one understanding task and three generation tasks for commits. The comprehensive experiments on these tasks demonstrate that CommitBARTsignificantly outperforms previous pre-trained works for code. Further analysis also reveals each pre-training task enhances the model performance.
@article{arxiv.2208.08100,
title = {CommitBART: A Large Pre-trained Model for GitHub Commits},
author = {Shangqing Liu and Yanzhou Li and Xiaofei Xie and Yang Liu},
journal= {arXiv preprint arXiv:2208.08100},
year = {2023}
}