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

Abbasi, Z., Esfandyari, S., & Rafe, V. (2018). Covering array generation using teaching learning base optimization algorithm. Tabriz Journal of Electrical Engineering, 48(1), 161-171. https://doi.org/10.1016/j.jss.2011.06.004
Ahmed, B. S., & Zamli, K. Z. (2011). A variable strength interaction test suites generation strategy using particle swarm optimization. Journal of Systems and Software, 84(12), 2171-2185. https://doi.org/10.1016/j.jss.2011.06.004
Akyol, S., & Alatas, B. (2017). Plant intelligence based metaheuristic optimization algorithms. Artificial Intelligence Review, 47(4), 417-462. https://doi.org/10.1016/j.jss.2011.06.004
Al-Betar, M. A. (2017). β-hill climbing: An exploratory local search. Neural Computing and Applications, 28(Suppl 1), 153-168.
Al-Sammarraie, H. N. N., & Jawawi, D. N. (2020). Multiple black hole inspired meta-heuristic searching optimization for combinatorial testing. Ieee Access, 8, 33406-33418. https://doi.org/10.1016/j.jss.2011.06.004
Alazzawi, A. K., Homaid, A. A. B., Alomoush, A. A., & Alsewari, A. A. (2017). Artificial bee colony algorithm for pairwise test generation. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-2), 103-108. https://doi.org/10.1016/j.jss.2011.06.004
Alazzawi, A. K., Rais, H. M., & Basri, S. (2018). Artificial bee colony algorithm for t-way test suite generation. In 2018 4th International Conference on Computer and Information Sciences (ICCOINS) (pp. 1-6). IEEE. https://doi.org/10.1016/j.jss.2011.06.004
Alazzawi, A. K., Rais, H. M., Basri, S., Alsariera, Y. A., Capretz, L. F., Balogun, A. O., & Imam, A. A. (2021). HABCSm: A hamming based t-way strategy based on hybrid artificial bee colony for variable strength test sets generation. arXiv preprint arXiv:2110.03728.
Alsewari, A. A., Xuan, L. M., & Zamli, K. Z. (2018). Firefly combinatorial testing strategy. In Science and Information Conference (pp. 936-944). Springer International Publishing. https://doi.org/10.1016/j.jss.2011.06.004
Che Rose, N. H., Othman, R. R., Zakaria, H. L., Suali, A. J., & Ahmad, Z. (2024). Wingsuit flying search optimization algorithm strategy for combinatorial T-Way test suite generation. International Journal of Advances in Soft Computing & Its Applications, 16(3). https://doi.org/10.1016/j.jss.2011.06.004
Cohen, D. M., Dalal, S. R., Fredman, M. L., & Patton, G. C. (1997). The AETG system: An approach to testing based on combinatorial design. IEEE Transactions on Software Engineering, 23(7), 437-444.  https://doi.org/10.1016/j.jss.2011.06.004
Cohen, M. B. (2004). Designing test suites for software interactions testing [Doctoral dissertation, University of Auckland].
Cohen, M. B., Dwyer, M. B., & Shi, J. (2007). Interaction testing of highly-configurable systems in the presence of constraints. In Proceedings of the 2007 international symposium on Software testing and analysis (pp. 129-139). https://doi.org/10.1145/1273463.1273482. https://doi.org/10.1016/j.jss.2011.06.004
Czerwonka, J. (2008). Pairwise testing in the real world: Practical extensions to test-case scenarios. Microsoft Corporation, Software Testing Technical Articles. https://doi.org/10.1016/j.jss.2011.06.004
Esfandyari, S., & Rafe, V. (2016). A Hybrid solution for Software testing to minimum test suite generation using hill climbing and bat search algorithms. Tabriz Journal of Electrical Engineering, 46(3), 25-35.
Esfandyari, S., & Rafe, V. (2018). A tuned version of genetic algorithm for efficient test suite generation in interactive t-way testing strategy. Information and Software Technology, 94, 165-185. https://doi.org/10.1016/j.jss.2011.06.004
Farasat, A., Menhaj, M. B., Mansouri, T., & Moghadam, M. R. S. (2010). ARO: A new model-free optimization algorithm inspired from asexual reproduction. Applied Soft Computing, 10(4), 1284-1292. https://doi.org/10.1016/j.jss.2011.06.004
Friedman, M. (1940). A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics, 11(1), 86-92. https://doi.org/10.1016/j.jss.2011.06.004
Gandomi, A. H., Yang, X.-S., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with computers, 29(1), 17-35. https://doi.org/10.1016/j.jss.2011.06.004
Hassan, A. A., Abdullah, S., Zamli, K. Z., & Razali, R. (2020). Combinatorial test suites generation strategy utilizing the whale optimization algorithm. IEEe Access, 8, 192288-192303. https://doi.org/10.1016/j.jss.2011.06.004
Hassan, A. A., Abdullah, S., Zamli, K. Z., & Razali, R. (2023). Q-learning whale optimization algorithm for test suite generation with constraints support. Neural Computing and Applications, 35(34), 24069-24090.
Jenkins, B. (2009). Jenny test tool. In. https://doi.org/10.1016/j.jss.2011.06.004
Karaboga, D. (2010). Artificial bee colony algorithm. Scholarpedia, 5(3), 6915.
Krishnan, R., Krishna, S. M., & Nandhan, P. S. (2007). Combinatorial testing: learnings from our experience. ACM SIGSOFT Software Engineering Notes, 32(3), 1-8. https://doi.org/10.1016/j.jss.2011.06.004
Lei, Y., Kacker, R., Kuhn, D. R., Okun, V., & Lawrence, J. (2007). IPOG: A general strategy for t-way software testing. In 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems (ECBS'07) (pp. 549-556). IEEE. https://doi.org/10.1016/j.jss.2011.06.004
Nie, C., & Leung, H. (2011). A survey of combinatorial testing. ACM Computing Surveys (CSUR), 43(2), 1-29.
Pira, E., Rafe, V., & Esfandyari, S. (2022). Minimum covering array generation using success-history and linear population size reduction based adaptive differential evolution algorithm. Tabriz Journal of Electrical Engineering, 52(2), 77-89.
Rao, R. V., Savsani, V. J., & Vakharia, D. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315. https://doi.org/10.1016/j.jss.2011.06.004
Selman, B., & Gomes, C. P. (2006). Hill-climbing search. Encyclopedia of Cognitive Science, 81(333–335), 10.
Singh, S. K., & Singh, A. (2012). Software Testing. Vandana Publications. https://doi.org/10.1016/j.jss.2011.06.004
Wilcoxon, F. (1992). Individual comparisons by ranking methods. In Breakthroughs in Statistics (pp. 196-202). Springer.
Williams, A. W., & Probert, R. L. (1996). A practical strategy for testing pair-wise coverage of network interfaces. In Proceedings of ISSRE'96: 7th International Symposium on Software Reliability Engineering (pp. 246-254). IEEE.
Yilmaz, C., Cohen, M. B., & Porter, A. A. (2006). Covering arrays for efficient fault characterization in complex configuration spaces. IEEE Transactions on Software Engineering, 32(1), 20-34. https://doi.org/10.1016/j.jss.2011.06.004
Zilberberg, C., Solé-Cava, A., & Klautau, M. (2006). The extent of asexual reproduction in sponges of the genus Chondrilla (Demospongiae: Chondrosida) from the Caribbean and the Brazilian coasts. Journal of Experimental Marine Biology and Ecology, 336(2), 211-220.
Send comment about this article
CAPTCHA Image
Journal of Engineering Management and Soft Computing

Articles in Press, Accepted Manuscript
Available Online from 01 October 2026

Files

Share

How to cite

Statistics