  Comments, or natural language descriptions of source code, are standard
practice among software developers. By communicating important aspects of the
code such as functionality and usage, comments help with software project
maintenance. However, when the code is modified without an accompanying
correction to the comment, an inconsistency between the comment and code can
arise, which opens up the possibility for developer confusion and bugs. In this
paper, we propose two models based on BERT (Devlin et al., 2019) and Longformer
(Beltagy et al., 2020) to detect such inconsistencies in a natural language
inference (NLI) context. Through an evaluation on a previously established
corpus of comment-method pairs both during and after code changes, we
demonstrate that our models outperform multiple baselines and yield comparable
results to the state-of-the-art models that exclude linguistic and lexical
features. We further discuss ideas for future research in using pretrained
language models for both inconsistency detection and automatic comment
updating.
