Fine-Grained Code-Comment Semantic Interaction Analysis

Abstract

Code comment, i.e., the natural language text to describe code, is considered as a killer for program comprehension. Current literature approaches mainly focus on comment generation or comment update, and thus fall short on explaining which part of the code leads to a specific content in the comment. In this paper, we propose that addressing such a challenge can better facilitate code under-standing. We propose Fosterer, which can build fine-grained se-mantic interactions between code statements and comment tokens. It not only leverages the advanced deep learning techniques like cross-modal learning and contrastive learning, but also borrows the weapon of pre-trained vision models. Specifically, it mimics the comprehension practice of developers, treating code statements as image patches and comments as texts, and uses contrastive learning to match the semantically-related part between the visual and tex-tual information. Experiments on a large-scale manually-labelled dataset show that our approach can achieve an Fl-score around 80%, and such a performance exceeds a heuristic-based baseline to a large extent. We also find that Fosterer can work with a high efficiency, i.e., it only needs 1.5 seconds for inferring the results for a code-comment pair. Furthermore, a user study demonstrates its usability: for 65% cases, its prediction results are considered as useful for improving code understanding. Therefore, our research sheds light on a promising direction for program comprehension.