Code Retrieval

Code retrieval focuses on efficiently finding relevant code snippets within large repositories based on natural language queries or partial code inputs. Current research emphasizes improving the accuracy and efficiency of retrieval using techniques like Retrieval Augmented Generation (RAG) with large language models (LLMs), contrastive learning to enhance embedding quality, and parameter-efficient fine-tuning of transformer models. These advancements are driven by the need for more robust benchmarks and the development of multilingual models to address data scarcity in certain programming languages, ultimately aiming to accelerate software development and improve software engineering tasks.