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Semantic code search is the task of retrieving relevant code snippet given anatural language query. Different from typical information retrieval tasks,code search requires to bridge the semantic gap between the programminglanguage and natural language, for better describing intrinsic concepts andsemantics. Recently, deep neural network for code search has been a hotresearch topic. Typical methods for neural code search first represent the codesnippet and query text as separate embeddings, and then use vector distance(e.g. dot-product or cosine) to calculate the semantic similarity between them.There exist many different ways for aggregating the variable length of code orquery tokens into a learnable embedding, including bi-encoder, cross-encoder,and poly-encoder. The goal of the query encoder and code encoder is to produceembeddings that are close with each other for a related pair of query and thecorresponding desired code snippet, in which the choice and design of encoderis very significant.  In this paper, we propose a novel deep semantic model which makes use of theutilities of not only the multi-modal sources, but also feature extractors suchas self-attention, the aggregated vectors, combination of the intermediaterepresentations. We apply the proposed model to tackle the CodeSearchNetchallenge about semantic code search. We align cross-lingual embedding formulti-modality learning with large batches and hard example mining, and combinedifferent learned representations for better enhancing the representationlearning. Our model is trained on CodeSearchNet corpus and evaluated on theheld-out data, the final model achieves 0.384 NDCG and won the first place inthis benchmark. Models and code are available athttps://github.com/overwindows/SemanticCodeSearch.git.

Learning Deep Semantic Model for Code Search using CodeSearchNet Corpus