Security Test Case Prioritization through Ant Colony Optimization Algorithm

Abstract

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems. One of the challenges in software security testing is test case prioritization, which aims to reduce redundancy in fault occurrences when executing test suites. By effectively applying test case prioritization, both the time and cost required for developing secure software can be reduced. This paper proposes a test case prioritization technique based on the Ant Colony Optimization (ACO) algorithm, a metaheuristic approach. The performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection (APFD) metric, comparing it with traditional techniques. It has been applied to a Mobile Payment Wallet application to validate the proposed approach. The results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD metric. The ACO-based technique achieves an APFD of approximately 76%, two percent higher than the second-best optimal ordering technique. These findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases, saving time and improving software security overall.