Learning to Update Natural Language Comments Based on Code Changes

Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Junyi Jessy Li, Raymond Mooney


Abstract
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.
Anthology ID:
2020.acl-main.168
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1853–1868
Language:
URL:
https://aclanthology.org/2020.acl-main.168
DOI:
10.18653/v1/2020.acl-main.168
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.168.pdf
Video:
 http://slideslive.com/38929204
Code
 panthap2/LearningToUpdateNLComments