Code Search

Code search aims to retrieve relevant code snippets from a large corpus based on natural language queries, improving software development efficiency. Current research focuses on enhancing semantic understanding through techniques like Retrieval Augmented Generation (RAG) with large language models (LLMs), contrastive learning, and graph neural networks (GNNs) to better capture code structure and semantics, addressing issues like modality misalignment and bias in search results. Improved datasets with more realistic queries and multiple valid code matches are also a key focus. These advancements have significant implications for developer productivity and the broader software engineering field by facilitating faster code reuse and improved code understanding.