Multimodal Representation for Neural Code Search

2 Jul 2021  ·  Jian Gu, Zimin Chen, Martin Monperrus ·

Semantic code search is about finding semantically relevant code snippets for a given natural language query. In the state-of-the-art approaches, the semantic similarity between code and query is quantified as the distance of their representation in the shared vector space. In this paper, to improve the vector space, we introduce tree-serialization methods on a simplified form of AST and build the multimodal representation for the code data. We conduct extensive experiments using a single corpus that is large-scale and multi-language: CodeSearchNet. Our results show that both our tree-serialized representations and multimodal learning model improve the performance of code search. Last, we define intuitive quantification metrics oriented to the completeness of semantic and syntactic information of the code data, to help understand the experimental findings.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Code Search CodeSearchNet - Python Uni-LCRS MRR 0.8707 # 1
Code Search CodeSearchNet - Ruby Uni-SBT MRR 0.3639 # 1

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