  Commit messages aid developers in their understanding of a continuously
evolving codebase. However, developers not always document code changes
properly. Automatically generating commit messages would relieve this burden on
developers. Recently, a number of different works have demonstrated the
feasibility of using methods from neural machine translation to generate commit
messages. This work aims to reproduce a prominent research paper in this field,
as well as attempt to improve upon their results by proposing a novel
preprocessing technique. A reproduction of the reference neural machine
translation model was able to achieve slightly better results on the same
dataset. When applying more rigorous preprocessing, however, the performance
dropped significantly. This demonstrates the inherent shortcoming of current
commit message generation models, which perform well by memorizing certain
constructs. Future research directions might include improving diff embeddings
and focusing on specific groups of commits.
