Software Vulnerability Prediction (SVP) is a data-driven technique for software quality assurance that has recently gained considerable attention in the Software Engineering research community. However, the difficulties of preparing Software Vulnerability (SV) related data is considered as the main barrier to industrial adoption of SVP approaches. Given the increasing, but dispersed, literature on this topic, it is needed and timely to systematically select, review, and synthesize the relevant peer-reviewed papers reporting the existing SV data preparation techniques and challenges. We have carried out a Systematic Literature Review (SLR) of SVP research in order to develop a systematized body of knowledge of the data preparation challenges, solutions, and the needed research. Our review of the 61 relevant papers has enabled us to develop a taxonomy of data preparation for SVP related challenges. We have analyzed the identified challenges and available solutions using the proposed taxonomy. Our analysis of the state of the art has enabled us identify the opportunities for future research. This review also provides a set of recommendations for researchers and practitioners of SVP approaches.

中文翻译：

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软件漏洞预测的数据准备：系统文献综述


软件漏洞预测（SVP）是一种用于软件质量保证的数据驱动技术，最近在软件工程研究界引起了相当大的关注。然而，准备软件漏洞 (SV) 相关数据的困难被认为是工业界采用 SVP 方法的主要障碍。鉴于有关该主题的文献不断增加但分散，有必要及时系统地选择、审查和综合报告现有 SV 数据准备技术和挑战的相关同行评审论文。我们对 SVP 研究进行了系统文献综述 (SLR)，以便开发有关数据准备挑战、解决方案和所需研究的系统化知识体系。我们对 61 篇相关论文的回顾使我们能够针对 SVP 相关挑战制定数据准备分类法。我们使用提议的分类法分析了已确定的挑战和可用的解决方案。我们对现有技术的分析使我们能够确定未来研究的机会。本综述还为 SVP 方法的研究人员和实践者提供了一系列建议。