Multimodal Representation for Neural Code Search
This work addresses code search for developers, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of semantic code search by improving vector space representations through tree-serialization on simplified ASTs and multimodal learning, resulting in enhanced performance on the CodeSearchNet corpus.
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.