Fast Bug Detection Algorithm for Identifying Potential Vulnerabilities in Juliet Test Cases

Abstract

Automated static analysis tools (ASATs) are one of the most widely used and effective ways of detecting bugs in Java code. ASATs helps to improve the security of software by detecting potential violations without executing the application. We have explored the existing automated static analysis techniques detection capabilities and noticed that, they are deficient in terms of processing time and generation of false warnings. Thus, the study proposed a Fast Bug Detection Algorithm (FBDA) to address the aforementioned deficiencies. Furthermore, we compared our results based on the FBDA to the existing automated static analysis tools. The main idea is to reduce the size of the code area to be investigated without compromising on quality and improve the processing time. Additionally, we tested the effectiveness of our framework using a designated subset of the Juliet Test Suite and the results show that our approach achieved a performance gain of 66% and can successfully detect bug patterns than existing static analysis tools. Our experimental analysis further shows that, the percentage of false positive obtained by our framework is 18.5%, which is much less than the percentage of false positive reported by ASATs.