We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies. We propose an approach that learns to correlate changes across two distinct language representations, to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications. We train and evaluate our model using a dataset that we collected from commit histories of open-source software projects, with each example consisting of a concurrent update to a method and its corresponding comment. We compare our approach against multiple baselines using both automatic metrics and human evaluation. Results reflect the challenge of this task and that our model outperforms baselines with respect to making edits.

PDF, Arxiv

In Proceedings of the 58th Annual Conference of the Association for Computational Linguistics (ACL), July 2020.

@inproceedings{panthaplackel:acl20, title={Learning to Update Natural Language Comments Based on Code Changes}, author={Sheena Panthaplackel and Pengyu Nie and Milos Gligoric and Junyi Jessy Li and Raymond J. Mooney}, booktitle={Proceedings of the 58th Annual Conference of the Association for Computational Linguistics (ACL)}, month={July}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127824", year={2020} }

Video