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.
Highlights
With the rapid development of information technology, software is playing an important role in various aspects all over the world, such as the economy, military, and society
We review the application of deep learning techniques in software security research, discuss the efforts of academia and related researchers in software security research, and look forward to the opportunities and challenges that deep learning technology faces in the field of software security
Automated patching technology was mainly used to prevent the spread of worms, and automated patching technology slowly penetrated into all aspects of computer software security with the development of technology. e automatic program repair technology can assist in the automatic repair of some defects in the software program, thereby effectively reducing the program debugging time of the software developer
Summary
With the rapid development of information technology, software is playing an important role in various aspects all over the world, such as the economy, military, and society. According to the statistics released by the Common Vulnerabilities and Exposures (CVE) organization [1], the number of software vulnerabilities discovered in 2000 was less than 4600 while the number of vulnerabilities currently covered almost nearly 20000 To summarize the work of this paper, the key contribution is three-folds: Firstly, we review the latest research progress of deep learning technology in software vulnerability detection, program patching, and defect prediction. We focus on the advantages and disadvantages of each technology from the aspects of software vulnerability detection, program patching, and defect prediction and propose ideas and solutions for future research. We look forward to the opportunities and challenges faced by existing automatic vulnerability detection, automatic program patching, and automatic defect prediction technologies and provide some reference value of future researchers
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