Generating Commit Messages from Git Diffs Sven van Hal Mathieu Post Kasper Wendel Delft University of Technology Delft University of Technology Delft University of Technology
[email protected] [email protected] [email protected] ABSTRACT be exploited by machine learning. The hypothesis is that methods Commit messages aid developers in their understanding of a con- based on machine learning, given enough training data, are able tinuously evolving codebase. However, developers not always doc- to extract more contextual information and latent factors about ument code changes properly. Automatically generating commit the why of a change. Furthermore, Allamanis et al. [1] state that messages would relieve this burden on developers. source code is “a form of human communication [and] has similar Recently, a number of different works have demonstrated the statistical properties to natural language corpora”. Following the feasibility of using methods from neural machine translation to success of (deep) machine learning in the field of natural language generate commit messages. This work aims to reproduce a promi- processing, neural networks seem promising for automated commit nent research paper in this field, as well as attempt to improve upon message generation as well. their results by proposing a novel preprocessing technique. Jiang et al. [12] have demonstrated that generating commit mes- A reproduction of the reference neural machine translation sages with neural networks is feasible. This work aims to reproduce model was able to achieve slightly better results on the same dataset. the results from [12] on the same and a different dataset. Addition- When applying more rigorous preprocessing, however, the per- ally, efforts are made to improve upon these results by applying a formance dropped significantly.