Abstract: Software security is fundamental to the stable operation of the digital society,as cybersecurity incidents caused by software vulnerabilities result in substantial economic losses.Traditional detection methods struggle to manage the increasing scale and complexity of code.In recent years,deep learning-based vulnerability detection techniques have made remarkable progress.The core challenge lies in encoding the syntactic,semantic,and structural information of source code into low-dimensional,con-tinuous vector representations to enable effective processing and analysis by deep learning models—a process known as code representation learning.This paper systematically reviews code representation learning techniques for vulnerability detection.It categorizes techniques into sequence-level,graph-level,and fusion-level approaches,evaluates the performance of deep learning-based methods in vulnerability detection,and identifies key challenges like dataset imbalance,poor cross-domain generalization,and li-mited model interpretability.The study reveals that representation methods integrating sequential and graph structural features improve the average F1-score by 15%~26% compared to single-modality approaches.Moreover,combining prompt learning with graph-guided strategies significantly enhances the vulnerability detection capabilities of LLMs.This review provides a structured reference framework for the software security community and identifies promising future directions such as federated learning,explainable AI(XAI),and cross-language representation learning.

Key words: Vulnerability detection, Code representation, Deep learning, Graph neural network, Pretrained language model, Explainable artificial intelligence