  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.
