Enhancing Semantic Code Search with Deep Graph Matching

The job of discovering appropriate code snippets against a natural language query is an important task for software developers. Appropriate retrieval increases productivity and quality as well. In contrast to traditional information techniques, search necessitates bridging the semantic breach between programming languages fragments. Deep neural networks codes have recently been hot topic in research. standard quest approaches present source form text independent embedding, then calculate similarity them using vector distance (e.g., cosine similarity). Although recent research utilized during search, it overlooked contained rich deep structural features them. this study, we are also dealing with problem by providing solution that facilitates developers development. Our proposed model effectively used graph matching searching approach retrieval. It first converts both fragments format module facilitate process will retrieve best-matched snippets. not only exploits enriched meanings features, but uses cross-attention mechanism learn fine-grained exists code. model’s evaluation done Codesearchnet dataset six representative languages. provides comparatively good results compared existing baselines. enables users find required snippets, ranking top 10 results. accuracy system approximately 97%.