On the Relevance of Cross-project Learning with Nearest Neighbours for Commit Message Generation
This work addresses the challenge of low-quality commit messages for software developers, but it is incremental as it builds on existing NNGen methods.
The paper tackled the problem of generating commit messages for software maintenance by showing that the nearest neighbor algorithm (NNGen) does not benefit from cross-project learning in most cases, and introduced a simpler, faster variation that outperforms NNGen with a BLEU_4 score improvement.
Commit messages play an important role in software maintenance and evolution. Nonetheless, developers often do not produce high-quality messages. A number of commit message generation methods have been proposed in recent years to address this problem. Some of these methods are based on neural machine translation (NMT) techniques. Studies show that the nearest neighbor algorithm (NNGen) outperforms existing NMT-based methods, although NNGen is simpler and faster than NMT. In this paper, we show that NNGen does not take advantage of cross-project learning in the majority of the cases. We also show that there is an even simpler and faster variation of the existing NNGen method which outperforms it in terms of the BLEU_4 score without using cross-project learning.