CoCoAST: Representing Source Code via Hierarchical Splitting and Reconstruction of Abstract Syntax Trees - Shi, Ensheng; Wang, Yanlin; Du, Lun; Zhang, Hongyu; Han, Shi; Zhang, Dongmei; Sun, Hongbin (Empirical Software Engineering, 2023)
Recently, machine learning techniques especially deep learning techniques have made substantial progress on some code intelligence tasks such as code summarization, code search, clone detection, etc. How to represent source code to effectively capture the syntactic, structural, and semantic information is a key challenge. Recent studies show that the information extracted from abstract syntax trees (ASTs) is conducive to code representation learning. However, existing approaches fail to fully capture the rich information in ASTs due to the large size/depth of ASTs. In this paper, we propose a novel model CoCoAST that hierarchically splits and reconstructs ASTs to comprehensively capture the syntactic and semantic information of code without the loss of AST structural information. First, we hierarchically split a large AST into a set of subtrees and utilize a recursive neural network to encode the subtrees. Then, we aggregate the embeddings of subtrees by reconstructing the split ASTs to get the representation of the complete AST. Finally, we combine AST representation carrying the syntactic and structural information and source code embedding representing the lexical information to obtain the final neural code representation. We have applied our source code representation to two common program comprehension tasks, code summarization and code search. Extensive experiments have demonstrated the superiority of CoCoAST. To facilitate reproducibility, our data and code are available https://github.com/s1530129650/CoCoAST.