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.

中文翻译：

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自动软件漏洞检测，程序修复和缺陷预测技术的概述

开源软件由于其开放性和灵活性已被广泛用于各个行业，但是它也带来了潜在的软件安全性问题。随着软件数量的大规模增加和复杂性的增加，传统的手动方法无法有效地解决这些安全问题，无法满足当前的网络空间安全要求。因此，开发更智能的技术应用于软件中潜在的安全问题是软件安全领域研究人员的重要研究课题。深度学习技术的发展为研究软件中潜在的安全问题带来了新的机遇，研究人员相继提出了许多自动化方法。在本文中，这些自动化技术将从三个方面进行详细评估和分析：软件漏洞检测，软件程序修复和软件缺陷预测。同时，我们指出了这些研究方法的一些问题，给出了相应的解决方案，并最终展望了深度学习技术在自动化软件漏洞检测，自动化程序修复和自动化缺陷预测中的应用前景。