A Hybrid Strategy Based on Asexual Reproduction Optimization and the Beta Hill-Climbing Algorithm for Generating an Optimal Test Suite

Document Type : Original Article

Authors

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

2 Department of Computer Engineering, Malayer University, Malayer, Iran

10.22091/jemsc.2026.15230.1344

Abstract

In testing software systems that involve a large number of input parameters and their various combinations, the phenomenon of “combinatorial explosion” often occurs. The t-way combinatorial testing method, by generating a set of test cases, seeks to provide the maximum possible coverage of parameter combinations. Constructing a covering array with minimal size is considered an optimization problem, and so far, various metaheuristic approaches such as Genetic Algorithms and Particle Swarm Optimization have been applied to solve it. Although these methods have succeeded in producing smaller arrays, challenges such as computational overhead and increasing array size still remain, requiring further optimization. In this paper, to enhance testing efficiency and reduce the volume of generated test cases, we employ a hybrid strategy based on Asexual Reproduction Optimization (ARO) and the Beta Hill-Climbing (BHC) algorithm to generate an optimal test suite. By leveraging the exploration capability of ARO and the exploitative capability of BHC, this hybrid approach can not only reduce the size of the covering array but also improve the accuracy and comprehensiveness of testing. Evaluation results and performance comparisons with several well-known methods show that the proposed strategy improves functional testing efficiency

Keywords

Main Subjects

References

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Journal of Engineering Management and Soft Computing
Volume 12, Issue 4 - Serial Number 25
October 2026
Pages 210-226

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