Survey of deep learning models for vulnerability detection

With the rapid development of information technology, software security issues have become increasingly prominent, especially the importance of software vulnerability detection technology, which has continued to increase. This paper reviews vulnerability detection technology based on neural networks, with a special focus on two widely used models: Devign and CodeBERT. By analyzing the working principles and processes of these two models, their performance and advantages in dealing with vulnerability detection tasks in complex codes are explored. With the advancement of technology, an analysis is conducted on the update and development of these two different technologies from various perspectives. At the same time, this paper also analyzes the challenges that existing models may encounter and proposes future development directions, including data processing, model design improvements, and innovations in feature extraction technology, in order to improve the accuracy and efficiency of vulnerability detection technology.