Time for addressing software security issues: prediction models and impacting factors

Abstract

Finding and fixing software vulnerabilities have become a major struggle for most software development companies. While generally without alternative, such fixing efforts are a major cost factor, which is why companies have a vital interest in focusing their secure software development activities such that they obtain an optimal return on this investment. We investigate, in this paper, quantitatively the major factors that impact the time it takes to fix a given security issue based on data collected automatically within SAP’s secure development process, and we show how the issue fix time could be used to monitor the fixing process. We use three machine learning methods and evaluate their predictive power in predicting the time to fix issues. Interestingly, the models indicate that vulnerability type has less dominant impact on issue fix time than previously believed. The time it takes to fix an issue instead seems much more related to the component in which the potential vulnerability resides, the project related to the issue, the development groups that address the issue, and the closeness of the software release date. This indicates that the software structure, the fixing processes, and the development groups are the dominant factors that impact the time spent to address security issues. SAP can use the models to implement a continuous improvement of its secure software development process and to measure the impact of individual improvements. The development teams at SAP develop different types of software, adopt different internal development processes, use different programming languages and platforms, and are located in different cities and countries. Other organizations, may use the results—with precaution—and be learning organizations.