Learning to Represent Programs with Heterogeneous Graphs

Code representation, which transforms programs into vectors with semantics, is essential for source code processing. We have witnessed the effectiveness of incorporating structural information (i.e., graph) into code representations in recent years. Specifically, the abstract syntax tree (AST) and t...

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Vydané v:2022 IEEE/ACM 30th International Conference on Program Comprehension (ICPC) s. 378 - 389
Hlavní autori: Zhang, Kechi, Wang, Wenhan, Zhang, Huangzhao, Li, Ge, Jin, Zhi
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: ACM 01.05.2022
Predmet:
code representation
Codes
Grammar
graph neural networks
heterogeneous graphs
Predictive models
Semantics
Syntactics
Transformers
Transforms
ISSN:2643-7171
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Popis
Shrnutí:Code representation, which transforms programs into vectors with semantics, is essential for source code processing. We have witnessed the effectiveness of incorporating structural information (i.e., graph) into code representations in recent years. Specifically, the abstract syntax tree (AST) and the AST-augmented graph of the program contain much structural and semantic information, and most existing studies apply them for code representation. The graph adopted by existing approaches is homogeneous, i.e., it discards the type information of the edges and the nodes lying within AST. That may cause plausible obstruction to the representation model. In this paper, we propose to leverage the type information in the graph for code representation. To be specific, we propose the heterogeneous program graph (HPG), which provides the types of the nodes and the edges explicitly. Furthermore, we employ the heterogeneous graph transformer (HGT) architecture to generate representations based on HPG, considering the type of information during processing. With the additional types in HPG, our approach can capture complex structural information, produce accurate and delicate representations, and finally perform well on certain tasks. Our in-depth evaluations upon four classic datasets for two typical tasks (i.e., method name prediction and code classification) demonstrate that the heterogeneous types in HPG benefit the representation models. Our proposed \text{HPG}+\text{HGT} also outperforms the SOTA baselines on the subject tasks and datasets.
ISSN:2643-7171
DOI:10.1145/3524610.3527905