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Security Test Case Prioritization through Ant Colony Optimization Algorithm

Abdulaziz Attaallah1, Khalil al-Sulbi2, Areej Alasiry3, Mehrez Marzougui3, Mohd Waris Khan4,*, Mohd Faizan4, Alka Agrawal5, Dhirendra Pandey5

1 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Department of Computer Science, Al-Qunfudah Computer College, Umm Al-Qura University, Mecca, Saudi Arabia
3 College of Computer Science, King Khalid University, Abha, 61421, Saudi Arabia
4 Department of Computer Application, Integral University, Lucknow, Uttar Pradesh, 226026, India
5 Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, Uttar Pradesh, 226025, India

* Corresponding Author: Mohd Waris Khan. Email: email

Computer Systems Science and Engineering 2023, 47(3), 3165-3195. https://doi.org/10.32604/csse.2023.040259

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.

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APA Style
Attaallah, A., al-Sulbi, K., Alasiry, A., Marzougui, M., Khan, M.W. et al. (2023). Security test case prioritization through ant colony optimization algorithm. Computer Systems Science and Engineering, 47(3), 3165-3195. https://doi.org/10.32604/csse.2023.040259
Vancouver Style
Attaallah A, al-Sulbi K, Alasiry A, Marzougui M, Khan MW, Faizan M, et al. Security test case prioritization through ant colony optimization algorithm. Comput Syst Sci Eng. 2023;47(3):3165-3195 https://doi.org/10.32604/csse.2023.040259
IEEE Style
A. Attaallah et al., "Security Test Case Prioritization through Ant Colony Optimization Algorithm," Comput. Syst. Sci. Eng., vol. 47, no. 3, pp. 3165-3195. 2023. https://doi.org/10.32604/csse.2023.040259



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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