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With the rapid increase in the amount of public code repositories, developersmaintain a great desire to retrieve precise code snippets by using naturallanguage. Despite existing deep learning based approaches(e.g., DeepCS andMMAN) have provided the end-to-end solutions (i.e., accepts natural language asqueries and shows related code fragments retrieved directly from code corpus),the accuracy of code search in the large-scale repositories is still limited bythe code representation (e.g., AST) and modeling (e.g., directly fusing thefeatures in the attention stage). In this paper, we propose a novel learnabledeep Graph for Code Search (calleddeGraphCS), to transfer source code intovariable-based flow graphs based on the intermediate representation technique,which can model code semantics more precisely compared to process the code astext directly or use the syntactic tree representation. Furthermore, we proposea well-designed graph optimization mechanism to refine the code representation,and apply an improved gated graph neural network to model variable-based flowgraphs. To evaluate the effectiveness of deGraphCS, we collect a large-scaledataset from GitHub containing 41,152 code snippets written in C language, andreproduce several typical deep code search methods for comparison. Besides, wedesign a qualitative user study to verify the practical value of our approach.The experimental results have shown that deGraphCS can achieve state-of-the-artperformances, and accurately retrieve code snippets satisfying the needs of theusers.

deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search