Code Search Model

Code search models aim to retrieve relevant code snippets from a large corpus based on natural language queries, improving software development efficiency. Current research focuses on improving the accuracy and efficiency of retrieval using techniques like contrastive learning, parameter-efficient fine-tuning of transformer models, and incorporating code structure (e.g., Abstract Syntax Trees) and semantics into model architectures. These advancements address biases in existing models and improve the overall user experience, impacting software development practices and fostering further research into robust and efficient code retrieval systems.