Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks

Mehrotra, Nikita; Agarwal, Navdha; Gupta, Piyush; Anand, Sandeep; Lo, David; Purandare, Rahul

doi:10.48550/arxiv.2011.11228

Cited by 1 publication

(1 citation statement)

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“…As stated in Section 1, how to represent source code effectively is a fundamental problem in software engineering research. Many source code representation techniques have achieved considerable success in many program comprehension tasks, including code classification [50,64,65], malware detection [11,66], and code generation [67,68]. We have described major source code representations in Section 2.2 and pointed out their limitations in Section 2.3.…”

Section: Related Workmentioning

confidence: 99%

HELoC: Hierarchical Contrastive Learning of Source Code Representation

Wang¹,

Wu²,

Zhang³

et al. 2022

Preprint

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syntax trees (ASTs) play a crucial role in source code representation. However, due to the large number of nodes in an AST and the typically deep AST hierarchy, it is challenging to learn the hierarchical structure of an AST effectively. In this paper, we propose HELoC, a hierarchical contrastive learning model for source code representation. To effectively learn the AST hierarchy, we use contrastive learning to allow the network to predict the AST node level and learn the hierarchical relationships between nodes in a self-supervised manner, which makes the representation vectors of nodes with greater differences in AST levels farther apart in the embedding space. By using such vectors, the structural similarities between code snippets can be measured more precisely. In the learning process, a novel GNN (called Residual Self-attention Graph Neural Network, RSGNN) is designed, which enables HELoC to focus on embedding the local structure of an AST while capturing its overall structure. HELoC is self-supervised and can be applied to many source code related downstream tasks such as code classification, code clone detection, and code clustering after pre-training. Our extensive experiments demonstrate that HELoC outperforms the state-of-the-art source code representation models. CCS CONCEPTS• Software and its engineering → Software maintenance tools.

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