  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.
