CSSAM: Code Search via Attention Matching of Code Semantics and Structures

Bo Cai, Yaoxiang Yu, Yi Hu

Abstract: The proper names of software engineering functions and methods can greatly assist developers in understanding and maintaining the code. Most researchers convert the method name generation task into the text summarization task. They take the token sequence and the abstract syntax tree (AST) of source code as input, and generate method names with a decoder. However, most proposed models learn semantic and structural features of the source code separately, resulting in poor performance in the method name generation task. Actually, each token in source code must have a corresponding node in its AST. Inspired by this observation, we propose SGMNG, a structure-guided method name generation model that learns the representation of two combined features. Additionally, we build a code graph called code relation graph (CRG) to describe the code structure clearly. CRG retains the structure of the AST of source code and contains data flows and control flows. SGMNG captures the semantic features of the code by encoding the token sequence and captures the structural features of the code by encoding the CRG. Then, SGMNG matches tokens in the sequence and nodes in the CRG to construct the combination of two features. We demonstrate the effectiveness of the proposed approach on the public dataset Java-Small with 700K samples, which indicates that our approach achieves significant improvement over the state-of-the-art baseline models in the ROUGE metric.