ESGen: Commit Message Generation Based on Edit Sequence of Code Change
Xiangping Chen, Yangzi Li, Zhicao Tang, Yuan Huang, Haojie Zhou, Mingdong Tang, Zibin Zheng · 2024
Commit messages provide important information for comprehending the code changes, and a number of researchers try to generate commit messages by using an automatic way. These research on commit message generation has profited from the code tokens or code structures such as AST. Since the edit sequence of code change is also important for capturing the code change intent, we propose a new commit message generation method called ESGen, which extracts AST edit sequences of code changes as model input. Specifically, we employ an O(ND) difference algorithm to extract the edit sequence from AST by comparing the ASTs before and after applying the code changes. Then, we construct a Bi-Encoder, which encodes the textual information and the AST edit sequence information of code change. The experimental results show that ESGen outperforms other baseline models, improving the BLEU-4 to 15.14. Also, when applying the edit sequence to 7 baseline models, they improve the BLEU-4 scores of these models by an average of 8.5%. Additionally, a human evaluation confirmed the effectiveness of ESGen in generating commit messages.