A Survey of Automatic Software Vulnerability Detection, Program Repair, and Defect Prediction Techniques. (30th September 2020)

• Record Type: Journal Article
• Authors: Shen, Zhidong; Chen, Si
• Other Names: Coppolino Luigi Academic Editor.
• Is Part Of: Security and communication networks. Volume 2020(2020)
• Publication Date: 2020-09-30
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2020/8858010
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Open source software has been widely used in various industries due to its openness and flexibility, but it also brings potential software security problems. Together with the large-scale increase in the number of software and the increase in complexity, the traditional manual methods to deal with these security issues are inefficient and cannot meet the current cyberspace security requirements. Therefore, it is an important research topic for researchers in the field of software security to develop more intelligent technologies to apply to potential security issues in software. The development of deep learning technology has brought new opportunities for the study of potential security issues in software, and researchers have successively proposed many automation methods. In this paper, these automation technologies are evaluated and analysed in detail from three aspects: software vulnerability detection, software program repair, and software defect prediction. At the same time, we point out some problems of these research methods, give corresponding solutions, and finally look forward to the application prospect of deep learning technology in automated software vulnerability detection, automated program repair, and automated defect prediction.