A Survey of the Full Process of Code Search Based on Deep Learning

ABSTRACT As a pivotal technology for enhancing software development efficiency, research on code search based on deep learning has emerged as a current hotspot. This review systematically deconstructs the entire code search process into four core stages: dataset construction, code preprocessing, heterogeneous representation model construction, and query expansion, while conducting an in‐depth analysis of the application status and challenges of deep learning technologies. In dataset construction, the Q and A pairs and C and D pairs relied on by deep learning models suffer from a lack of standardization. For example, CodeSearchNet exhibits insufficient cross‐lingual versatility, and CoDesc has incomplete noise filtering. During the code preprocessing stage, bottlenecks such as AST granularity selection and sequence information redundancy restrict the efficiency of feature extraction. Although the introduction of transformer and graph neural networks has optimized structural representation, a unified evaluation mechanism is lacking. In the research of heterogeneous representation models, while LSTM, CNN, and pretrained models (such as CodeBERT) effectively narrow the semantic gap, their cross‐domain search accuracy is insufficient. In terms of query expansion, deep learning‐based keyword expansion and intent completion methods struggle to capture users' real needs due to low semantic alignment accuracy. This review proposes, for the first time, a standardized dataset construction framework integrating multimodal data, a syntax‐semantic dual‐layer preprocessing evaluation mechanism, a cross‐domain transfer representation model, and a large language model‐driven intent dynamic expansion scheme. These contributions lay a theoretical foundation for the systematic development of code search technologies and provide cross‐task methodological references for related fields such as code clone detection.