Data Preparation for Software Vulnerability Prediction: A Systematic Literature Review

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 remains as the main barrier to industrial adoption. Despite this problem, there have been no systematic efforts to analyse the existing SV data preparation techniques and challenges. Without such insights, we are unable to overcome the challenges and advance this research domain. Hence, we are motivated to conduct a Systematic Literature Review (SLR) of SVP research to synthesize and gain an understanding of the data considerations, challenges and solutions that SVP researchers provide. From our set of primary studies, we identify the main practices for each data preparation step. We then present a taxonomy of 16 key data challenges relating to six themes, which we further map to six categories of solutions. However, solutions are far from complete, and there are several ill-considered issues. We also provide recommendations for future areas of SV data research. Our findings help illuminate the key SV data practices and considerations for SVP researchers and practitioners, as well as inform the validity of the current SVP approaches.