Towards data-driven vulnerability prediction for requirements

Abstract

Due to the abundance of security breaches we continue to see, the software development community is recently paying attention to a more proactive approach towards security. This includes predicting vulnerability before exploitation employing static code analysis and machine learning techniques. Such mechanisms, however, are designed to detect post-implementation vulnerabilities. As the root of a vulnerability can often be traced back to the requirement specification, and vulnerability discovered later in the development life cycle is more expensive to fix, we need additional preventive mechanisms capable of predicting vulnerability at a much earlier stage. In this paper, we propose a novel framework providing an automated support to predict vulnerabilities for a requirement as early as during requirement engineering. We further present a preliminary demonstration of our framework and the promising results we observe clearly indicate the value of this new research idea.