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Commit messages play an important role in software maintenance and evolution.Nonetheless, developers often do not produce high-quality messages. A number ofcommit message generation methods have been proposed in recent years to addressthis 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 fasterthan NMT. In this paper, we show that NNGen does not take advantage ofcross-project learning in the majority of the cases. We also show that there isan even simpler and faster variation of the existing NNGen method whichoutperforms it in terms of the BLEU_4 score without using cross-projectlearning.

On the Relevance of Cross-project Learning with Nearest Neighbours for Commit Message Generation