A Multi-Perspective Architecture for Semantic Code Search
This addresses the need for effective natural language search interfaces in software development, though it appears incremental as it builds on prior text-to-text matching models.
The paper tackles the problem of matching code to natural language descriptions for software repository search by proposing a multi-perspective cross-lingual neural framework, which achieves better performance than previous single-embedding approaches on the CoNaLa dataset.
The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories. In this paper, we propose a novel multi-perspective cross-lingual neural framework for code--text matching, inspired in part by a previous model for monolingual text-to-text matching, to capture both global and local similarities. Our experiments on the CoNaLa dataset show that our proposed model yields better performance on this cross-lingual text-to-code matching task than previous approaches that map code and text to a single joint embedding space.