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Within the realm of advanced code retrieval, existing methods have primarilyrelied on intricate matching and attention-based mechanisms. However, thesemethods often lead to computational and memory inefficiencies, posing asignificant challenge to their real-world applicability. To tackle thischallenge, we propose a novel approach, the Hyperbolic Code QA Matching(HyCoQA). This approach leverages the unique properties of Hyperbolic space toexpress connections between code fragments and their corresponding queries,thereby obviating the necessity for intricate interaction layers. The processcommences with a reimagining of the code retrieval challenge, framed within aquestion-answering (QA) matching framework, constructing a dataset with triplematches characterized as \texttt{<}negative code, description, positivecode\texttt{>}. These matches are subsequently processed via a static BERTembedding layer, yielding initial embeddings. Thereafter, a hyperbolic embeddertransforms these representations into hyperbolic space, calculating distancesbetween the codes and descriptions. The process concludes by implementing ascoring layer on these distances and leveraging hinge loss for model training.Especially, the design of HyCoQA inherently facilitates self-organization,allowing for the automatic detection of embedded hierarchical patterns duringthe learning phase. Experimentally, HyCoQA showcases remarkable effectivenessin our evaluations: an average performance improvement of 3.5\% to 4\% comparedto state-of-the-art code retrieval techniques.

Hyperbolic Code Retrieval: A Novel Approach for Efficient Code Search Using Hyperbolic Space Embeddings