Code Comment Inconsistency Detection with BERT and Longformer
This addresses a specific issue in software maintenance for developers, but it is incremental as it builds on existing methods and datasets.
The paper tackles the problem of detecting inconsistencies between code and comments when code is modified, proposing BERT and Longformer models that outperform baselines and achieve results comparable to state-of-the-art models without linguistic features.
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.