An Improved Jaya Algorithm-Based Strategy for T-Way Test Suite Generation

  • Abdullah B. NasserEmail author
  • Fadhl Hujainah
  • AbdulRahman A. Al-Sewari
  • Kamal Z. Zamli
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)


In the field of software testing, several meta-heuristics algorithms have been successfully used for finding an optimized t-way test suite (where t refers to covering level). T-way testing strategies adopt the meta-heuristic algorithms to generate the smallest/optimal test suite. However, the existing t-way strategies’ results show that no single strategy appears to be superior in all problems. The aim of this paper to propose a new variant of Jaya algorithm for generating t-way test suite called Improved Jaya Algorithm (IJA). In fact, the performance of meta-heuristic algorithms highly depends on the intensification and diversification capabilities. IJA enhances the intensification and diversification capabilities by introducing new operators search such lévy flight and mutation operator in Jaya Algorithm. Experimental results show that the IJA variant improves the results of original Jaya algorithm, also overcomes the problems of slow convergence of Jaya algorithm.


T-way testing Meta-heuristics Jaya Algorithm Improved Jaya algorithm 



The work reported in this paper is funded by Universiti Malaysia Pahang (UMP) grants “Optimization using Bee Colony” and “Prioritized t-way Test Suite Generation based on Chaotic Flower Pollination Algorithm”. We thank UMP for the contribution and supports.


  1. 1.
    Yang, X.-S., Nature-Inspired Metaheuristic Algorithms. 2 edn. Luniver Press (2010)Google Scholar
  2. 2.
    Yang, X.S.: Nature-Inspired Optimization Algorithms. Elsevier, Amsterdam (2014)zbMATHGoogle Scholar
  3. 3.
    Nasser, A.B., Zamli, K.Z., Alsewari, A.A., Ahmed, B.S.: Hybrid flower pollination algorithm strategies for t-way test suite generation. PLoS One 13(5), e0195187 (2018)CrossRefGoogle Scholar
  4. 4.
    Zamli, K.Z., Alkazemi, B.Y., Kendall, G.: A tabu search hyper-heuristic strategy for t-way test suite generation. Appl. Soft Comput. 44, 57–74 (2016)CrossRefGoogle Scholar
  5. 5.
    Ahmed, B.S., Zamli, K.Z., Lim, C.P.: Constructing a t-way interaction test suite using the particle swarm optimization approach. Int. J. Innov. Comput. Inform. Control 8(1), 431–452 (2012)Google Scholar
  6. 6.
    Alsewari, A.R.A., Zamli, K.Z.: Design and implementation of a harmony-search-based variable-strength t-way testing strategy with constraints support. Inf. Softw. Technol. 54(6), 553–568 (2012). (in English)CrossRefGoogle Scholar
  7. 7.
    Ahmed, B.S., Abdulsamad, T.S., Potrus, M.Y.: Achievement of minimized combinatorial test suite for configuration-aware software functional testing using the cuckoo search algorithm. Inf. Softw. Technol. 66, 13–29 (2015). (in English)CrossRefGoogle Scholar
  8. 8.
    Nasser, A.B., Alsewari, A.A., Tairan, N.M., Zamli, K.Z.: Pairwise test data generation based on flower pollination algorithm. Malays. J. Comput. Sci. 30(3), 242–257 (2017)CrossRefGoogle Scholar
  9. 9.
    Rao, R.: Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7(1), 19–34 (2016)Google Scholar
  10. 10.
    Yang, X.-S., Deb, S.: Cuckoo search via lévy flights. In: World Congress on Nature and Biologically Inspired Computing, pp. 210–214. IEEE (2009)Google Scholar
  11. 11.
    Kuhn, D.R., Kacker, R.N., Lei, Y.: Practical combinatorial testing. National Institute of Standards and Technology (NIST) Special Publication, vol. 800, p. 142 (2010)Google Scholar
  12. 12.
    Hartman, A., Raskin, L.: Problems and algorithms for covering arrays. Discrete Math. 284(1), 149–156 (2004)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lei, Y., Kacker, R., Kuhn, D.R., Okun, V., Lawrence, J.: 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 2007, pp. 549–556. IEEE (2007)Google Scholar
  14. 14.
    Cohen, D.M., Dalal, S.R., Fredman, M.L., Patton, G.C.: The AETG system: an approach to testing based on combinatorial design. IEEE Trans. Softw. Eng. 23(7), 437–444 (1997)CrossRefGoogle Scholar
  15. 15.
    Jenkins, B.: Jenny download page (2003). Accessed 16 Dec 2014
  16. 16.
    Williams, A.: TConfig download page. University of Ottawa (2008).[Accessed Accessed 23 Dec 2014
  17. 17.
    Hartman, A., Klinger, T., Raskin, L.: IBM intelligent test case handler. Discrete Math. 284(1), 149–156 (2010)Google Scholar
  18. 18.
    Miller, W., Spooner, D.L.: Automatic generation of floating-point test data. IEEE Trans. Softw. Eng. 3, 223–226 (1976)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Shiba, T., Tsuchiya, T., Kikuno, T.: Using artificial life techniques to generate test cases for combinatorial testing. In: Proceedings of the 28th Annual International Computer Software and Applications Conference, COMPSAC 2004, pp. 72–77. IEEE (2004)Google Scholar
  20. 20.
    Ahmed, B.S., Zamli, K.Z.: A review of covering arrays and their application to software testing. J. Comput. Sci. 7(9), 1375–1385 (2011)CrossRefGoogle Scholar
  21. 21.
    Nasser, A.B., Zamli, K.Z., Alsewari, A.A., Ahmed, B.S.: An elitist-flower pollination-based strategy for constructing sequence and sequence-less t-way test suite. Int. J. Bio-Inspired Comput. 12(2), 115–127 (2018)CrossRefGoogle Scholar
  22. 22.
    Nasser, A.B., Alsewari, A.R.A., Zamli, K.Z.: PairCS: a new approach of pairwise testing based on cuckoo search algorithm. Presented at the SOFTEC Asia 2015, Kuantan, Malaysia (2015)Google Scholar
  23. 23.
    Zamli, K.Z., Din, F., Baharom, S., Ahmed, B.S.: Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites. Eng. Appl. Artif. Intell. 59, 35–50 (2017)CrossRefGoogle Scholar
  24. 24.
    Zamli, K.Z., Din, F., Kendall, G., Ahmed, B.S.: An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation. Inf. Sci. 399, 121–153 (2017)CrossRefGoogle Scholar
  25. 25.
    Zamli, K.Z., Din, F., Ahmed, B.S., Bures, M.: A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem. PLoS One 13(5), e0195675 (2018)CrossRefGoogle Scholar
  26. 26.
    Pavlyukevich, I.: Lévy flights, non-local search and simulated annealing. J. Comput. Phys. 226(2), 1830–1844 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Abdullah B. Nasser
    • 1
    Email author
  • Fadhl Hujainah
    • 1
  • AbdulRahman A. Al-Sewari
    • 1
  • Kamal Z. Zamli
    • 1
  1. 1.Faculty of Computer Systems and Software EngineeringUniversiti Malaysia PahangKuantanMalaysia

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