Code Retrieval with Mixture of Experts Prototype Learning Based on Classification

The semantic connection between code and queries is crucial for code retrieval, but many human-written queries fail to accurately capture the code’s core intent, leading to ambiguity. This ambiguity complicates the code search process, as the queries do not provide a clear overview of the code’s purpose. Our analysis reveals that while ambiguous queries may not precisely summarize the intent of the code, they often share the same general topics as the corresponding code. In light of this discovery, we propose Code Retrieval with Mixture of Experts Prototype Learning Based on Classification (CRME), a novel approach that combines classification for prototype-based representation learning and result ensembling. CRME utilizes specialized pre-trained models focused on the specific domains of ambiguous queries. It consists of two key components: Multiple Classification Prototype and Representation Learning with a Prototype-based Multi-model Contrastive (PMC) Loss during training, and Multi-Prototype Mixture of Experts Integration (MP-MoE) module for fine-grained ensemble inference. Our method can effectively address the issue of query ambiguity and improves search precision. Experimental results on the CodeSearchNet dataset, covering six sub-datasets, show that CRME outperforms existing methods, achieving an average MRR score of 81.4%. When applied to pre-trained models like CodeBERT, GraphCodeBERT, UniXcoder and CodeT5+, CRME can effectively boosts their performances.