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Code retrieval is to find the code snippet from a large corpus of source coderepositories that highly matches the query of natural language description.Recent work mainly uses natural language processing techniques to process bothquery texts (i.e., human natural language) and code snippets (i.e., machineprogramming language), however neglecting the deep structured features of querytexts and source codes, both of which contain rich semantic information. Inthis paper, we propose an end-to-end deep graph matching and searching (DGMS)model based on graph neural networks for the task of semantic code retrieval.To this end, we first represent both natural language query texts andprogramming language code snippets with the unified graph-structured data, andthen use the proposed graph matching and searching model to retrieve the bestmatching code snippet. In particular, DGMS not only captures more structuralinformation for individual query texts or code snippets but also learns thefine-grained similarity between them by cross-attention based semantic matchingoperations. We evaluate the proposed DGMS model on two public code retrievaldatasets with two representative programming languages (i.e., Java and Python).Experiment results demonstrate that DGMS significantly outperformsstate-of-the-art baseline models by a large margin on both datasets. Moreover,our extensive ablation studies systematically investigate and illustrate theimpact of each part of DGMS.

Deep Graph Matching and Searching for Semantic Code Retrieval