Aerospace Defense

Optimization of Combinatorial Test Scenarios for Air Defense Systems Using Covering Arrays

Document Type : Original Article

Authors

sajad esfandyari 1
Einollah Pira 2
Leila Yousofvand 3

1 Department of Computer Engineering, Faculty of Engineering, Malayer University, Malayer, Iran

2 Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.

3 Department of Computer Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran

Abstract
The performance of air defense and cyber defense systems is highly dependent on the interactions among multiple operational parameters. However, exhaustive evaluation of all possible parameter configurations is computationally infeasible due to the exponential growth in the number of combinations. To address this challenge, this paper proposes a covering array-based testing approach that leverages an Improved Ant Colony Optimization (ACO) algorithm to generate an efficient set of test scenarios. By incorporating enhanced path selection mechanisms and adaptive pheromone updating strategies, the proposed algorithm achieves faster convergence and higher coverage quality compared to the classical ACO. The method was validated on a simulated multi-parameter air defense system, where the results indicate a reduction of more than 90% in the required test cases while ensuring complete coverage of all critical t-way interactions. Furthermore, the proposed approach is adaptable to the testing of command-and-control systems, electronic warfare platforms, and cyber defense networks.

Keywords

Combinatorial Testing
Covering Arrays
Improved ACO
Air Defense
Test Optimization

Subjects

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Aerospace Defense
Volume 4, Issue 2
Spring 2025
Pages 87-106

  • Receive Date 24 August 2025
  • Revise Date 19 December 2025
  • Accept Date 12 February 2026

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