  Code search aims to retrieve the code snippet that highly matches the given
query described in natural language. Recently, many code pre-training
approaches have demonstrated impressive performance on code search. However,
existing code search methods still suffer from two performance constraints:
inadequate semantic representation and the semantic gap between natural
language (NL) and programming language (PL). In this paper, we propose CPLCS, a
contrastive prompt learning-based code search method based on the cross-modal
interaction mechanism. CPLCS comprises:(1) PL-NL contrastive learning, which
learns the semantic matching relationship between PL and NL representations;
(2) a prompt learning design for a dual-encoder structure that can alleviate
the problem of inadequate semantic representation; (3) a cross-modal
interaction mechanism to enhance the fine-grained mapping between NL and PL. We
conduct extensive experiments to evaluate the effectiveness of our approach on
a real-world dataset across six programming languages. The experiment results
demonstrate the efficacy of our approach in improving semantic representation
quality and mapping ability between PL and NL.
