Mining the Categorized Software Repositories to Improve the Analysis of Security Vulnerabilities

Security has become the Achilles' heel of most modern software systems. Techniques ranging from the manual inspection to automated static and dynamic analyses are commonly employed to identify security vulnerabilities prior to the release of the software. However, these techniques are time consuming and cannot keep up with the complexity of ever-growing software repositories (e.g., Google Play and Apple App Store). In this paper, we aim to improve the status quo and increase the efficiency of static analysis by mining relevant information from vulnerabilities found in the categorized software repositories. The approach relies on the fact that many modern software systems are developed using rich application development frameworks (ADF), allowing us to raise the level of abstraction for detecting vulnerabilities and thereby making it possible to classify the types of vulnerabilities that are encountered in a given category of application. We used open-source software repositories comprising more than 7 million lines of code to demonstrate how our approach can improve the efficiency of static analysis, and in turn, vulnerability detection.