Vulnerabilities in software source code are one of the critical issues in the realm of software code auditing. Due to their high impact, several approaches have been studied in the past few years to mitigate the damages from such vulnerabilities. Among the approaches, deep learning has gained popularity throughout the years to address such issues. In this literature survey, the authors provide an extensive review of the many works in the field software vulnerability analysis that utilise deep learning-based techniques. The reviewed works are systemised according to their objectives (i.e. the type of vulnerability analysis aspect), the area of focus (i.e. the focus area of the analysis), what information about source code is used (i.e. the features), and what deep learning techniques they employ (i.e. what algorithm is used to process the input and produce the output). They also study the limitations of the papers and topical trends concerning vulnerability analysis.

中文翻译：

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基于深度学习的漏洞分析文献综述

软件源代码中的漏洞是软件代码审核领域中的关键问题之一。由于它们的巨大影响，在过去几年中研究了几种方法来减轻此类漏洞的损害。在这些方法中，多年来，深度学习已逐渐普及以解决此类问题。在此文献调查中，作者对利用基于深度学习的技术的现场软件漏洞分析中的许多作品进行了广泛的回顾。根据目标（即漏洞分析方面的类型），重点领域（即分析的重点领域），使用了哪些有关源代码的信息（即功能）以及进行了哪些深度学习，对审阅的作品进行系统化他们采用的技术（即 使用哪种算法来处理输入并产生输出）。他们还研究了论文的局限性以及与脆弱性分析有关的主题趋势。