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deGraphCS : Embedding Variable-based Flow Graph for Neural Code Search by Chen Zeng; Yue Yu; Shanshan Li; Xin Xia; Zhiming Wang; Mingyang Geng; Linxiao Bai; Wei Dong; Xiangke Liao is a Computer Science article available to read on EtoBox.
What is deGraphCS : Embedding Variable-based Flow Graph for Neural Code Search about?
With the rapid increase of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language. Despite existing deep learning-based approaches provide end-to-end solutions ( i.e. , accept natural language as queries and show related code fragments), the performance of code search in the large-scale repositories is still low in accuracy because of the code representation ( e.g. , AST) and modeling ( e.g. , directly fusing features in the attention stage). In this paper, we propose a novel learnable de ep G raph for C ode S earch (called de G raph CS ) to transfer source code into variable-based flow graphs based on an intermediate representation technique, which can model code semantics more precisely than directly processing the code as text or using the syntax tree representation. Furthermore, we propose a graph optimization mechanism to refine the code representation and apply an improved gated graph neural network to model variable-based flow graphs. To evaluate the effectiveness of de G raph CS , we collect a large-scale dataset from GitHub containing 41,152 code snippets written in the C language and reproduce several typic
Who reads deGraphCS : Embedding Variable-based Flow Graph for Neural Code Search?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Chen Zeng; Yue Yu; Shanshan Li; Xin Xia; Zhiming Wang; Mingyang Geng; Linxiao Bai; Wei Dong; Xiangke Liao
- Publisher
- ACM
- Published
- 2022
- Language
- EN
- Field
- Computer Science (Physical Sciences)