Publication Type

Journal Article

Version

publishedVersion

Publication Date

9-2022

Abstract

Code representation is a fundamental problem in many software engineering tasks. Despite the effort made by many researchers, it is still hard for existing methods to fully extract syntactic, structural and sequential features of source code, which form the hierarchical semantics of the program and are necessary to achieve a deeper code understanding. To alleviate this difficulty, we propose a new supervised approach based on the novel use of Tree-LSTM to incorporate the sequential and the global semantic features of programs explicitly into the representation model. Unlike previous techniques, our proposed model can not only learn low-level syntactic information within each statement but also the high-level semantic information between statements over the constructed semantic graph. Besides, considering that the sequential semantics is also critical for developers to understand the dependency path and data flow transmission, we propose a DFS-based method to generate the topological order of statements being processed, and then feed them as well as their in-neighboring information and syntactic embeddings into the proposed model to learn richer statement-level semantic features. Extensive experiments on multiple program comprehension tasks, e.g., code clone detection, demonstrate that our method achieves promising performance compared with other existing baselines.

Keywords

Code representation, Graph-LSTM, Hierarchical semantics, Program classification, Clone detection, Vulnerability detection, Deep learning

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Journal of Systems and Software

Volume

191

First Page

1

Last Page

21

ISSN

0164-1212

Identifier

10.1016/j.jss.2022.111355

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.jss.2022.111355

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