Skip to main content
SpringerLink
Log in

Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation

  • Conference paper
  • First Online:
Recent Trends in Data Science and Soft Computing (IRICT 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 843))

Abstract

The performance of meta-heuristic algorithms is highly dependents on the fine balance between intensification and diversification. Too much intensification may result in the quick loss of diversity and aggressive diversification may lead to inefficient search. Therefore, there is a need for proper parameter controls to balance out between intensification and diversification. The challenge here is to find the best values for the control parameters to achieve acceptable results. Many studies focus on tuning of the control-parameters and ignore the common parameter, that is, the population size. Addressing this issue, this paper proposes self-adaptive population size strategy based on Flower Pollination Algorithm, called saFPA for t-way test suite generation. In the proposed algorithm, the population size of FPA is dynamically varied based on the current need of the search process. Experimental results show that saFPA produces very competitive results as compared to existing strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Glover, F.: Tabu search-part I. ORSA J. Comput. 1(3), 190–206 (1989)

    Article  Google Scholar 

  2. Kirkpatrick, S.: Optimization by simulated annealing: quantitative studies. J. Stat. Phys. 34(6), 975–986 (1984)

    Article  MathSciNet  Google Scholar 

  3. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–72 (1992)

    Article  Google Scholar 

  4. Dorigo, M., et al.: Ant colony optimization and swarm intelligence. In: 6th International Conference Ants - Theoretical Computer Science and General Issues. Springer, Berlin (2008)

    Google Scholar 

  5. Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Berlin (2011)

    Google Scholar 

  6. Feoktistov, V.: Differential Evolution. Springer, Berlin (2006)

    MATH  Google Scholar 

  7. Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194(36), 3902–3933 (2005)

    Article  Google Scholar 

  8. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)

    Article  Google Scholar 

  9. Pham, D., et al:. The bees algorithm–a novel tool for complex optimisation. In: International Conference on Intelligent Production Machines and Systems (2011)

    Google Scholar 

  10. Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature and Biologically Inspired Computing. IEEE (2009)

    Google Scholar 

  11. Yang, X.-S.: Flower pollination algorithm for global optimization. In: International Conference on Unconventional Computing and Natural Computation. Springer (2012)

    Google Scholar 

  12. Yang, X.-S.: Firefly algorithm, Lévy flights and global optimization. In: Ellis, R., Petridis, M. (eds.) Research and Development in Intelligent Systems, vol. 26, pp. 209–218. Springer, Berlin (2010)

    Google Scholar 

  13. Nasser, A.B., Alsewari, A.R.A., Zamli, K.Z.: Parameter free flower algorithm based strategy for pairwise testing. In: 7th International Conference on Software and Computer Applications, Kuantan, Malaysia (2018)

    Google Scholar 

  14. Kuhn, D.R., Wallace, D.R., Gallo, J.A.M.: Software fault interactions and implications for software testing. IEEE Trans. Softw. Eng. 30(6), 418–421 (2004)

    Article  Google Scholar 

  15. Chen, X., et al.: Applying particle swarm optimization to pairwise testing. In: 2010 IEEE 34th Annual Computer Software and Applications Conference (COMPSAC). IEEE (2010)

    Google Scholar 

  16. Williams, A.W.: Determination of test configurations for pair-wise interaction coverage. In: Testing of Communicating Systems, pp. 59–74. Springer, Berlin (2000)

    Google Scholar 

  17. Hartman, A., Raskin, L.: Problems and algorithms for covering arrays. Discrete Math. 284(1), 149–156 (2004)

    Article  MathSciNet  Google Scholar 

  18. Mandl, R.: Orthogonal latin squares: an application of experiment design to compiler testing. Commun. ACM 28(10), 1054–1058 (1985)

    Article  Google Scholar 

  19. Bush, K.A.: Orthogonal arrays of index unity. Ann. Math. Stat. 23(3), 426–434 (1952)

    Article  MathSciNet  Google Scholar 

  20. Lei, Y., et al.: IPOG/IPOG-D: efficient test generation for multi-way combinatorial testing. Softw. Test. Verif. Reliab. 18(3), 125–148 (2008)

    Article  Google Scholar 

  21. Lei, Y., et al.: 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. IEEE (2007)

    Google Scholar 

  22. Forbes, M., et al.: Refining the in-parameter-order strategy for constructing covering arrays. J. Res. Nat. Inst. Stand. Technol. 113(5), 287 (2008)

    Article  Google Scholar 

  23. Williams, A.: TConfig download page (2008)

    Google Scholar 

  24. Cohen, D.M., et al.: The AETG system: an approach to testing based on combinatorial design. IEEE Trans. Softw. Eng. 23(7), 437–444 (1997)

    Article  Google Scholar 

  25. Hartman, A., Klinger, T., Raskin, L.: IBM intelligent test case handler. Discrete Math. 284(1), 149–156 (2010)

    Google Scholar 

  26. Jenkins, B.: Jenny Tool download page (2003). http://www.burtleburtle.net/bob/math. Accessed 16 Dec 2014

  27. Nie, C., Leung, H.: A survey of combinatorial testing. ACM Comput. Surv. (CSUR) 43(2), 11 (2011)

    Article  Google Scholar 

  28. 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. IEEE (2004)

    Google Scholar 

  29. Colbourn, C.J., Cohen, M.B., Turban, R.: A deterministic density algorithm for pairwise interaction coverage. In: IASTED Conference on Software Engineering. Citeseer (2004)

    Google Scholar 

  30. Ahmed, B.S., Zamli, K.Z.: A variable strength interaction test suites generation strategy using particle swarm optimization. J. Syst. Softw. 84(12), 2171–2185 (2011)

    Article  Google Scholar 

  31. Alsewari, A.R.A., Zamli, K.Z.: Design and implementation of a harmony-search-based variable-strength tway testing strategy with constraints support. Inf. Softw. Technol. 54(6), 553–568 (2012)

    Article  Google Scholar 

  32. Nasser, A.B., et al.: Pairwise test data generation based on flower pollination algorithm. Malay. J. Comput. Sci. 30(3), 242–257 (2017)

    Article  Google Scholar 

  33. Nasser, A.B., et al.: Assessing optimization based strategies for t-way test suite generation: the case for flower-based strategy. In: 5th IEEE International Conference on Control Systems, Computing and Engineering, Pinang, Malaysia (2015)

    Google Scholar 

  34. 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)

    Article  Google Scholar 

  35. Nasser, A.B., et al.: Hybrid flower pollination algorithm strategies for t-way test suite generation. PLoS ONE 13(5), e0195187 (2018)

    Article  Google Scholar 

  36. 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)

    Article  Google Scholar 

  37. Zamli, K.Z., et al.: An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation. Inf. Sci. 399, 121–153 (2017)

    Article  Google Scholar 

  38. Zamli, K.Z., et al.: A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem. PLoS ONE 13(5), e0195675 (2018)

    Article  Google Scholar 

  39. Zamli, K.Z., et al.: 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)

    Article  Google Scholar 

Download references

Acknowledgements

The work reported in this paper is funded by Fundamental Research Grant from Ministry of Higher Education Malaysia titled: A Reinforcement Learning Sine Cosine based Strategy for Combinatorial Test Suite Generation. We thank MOHE for the contribution and supports, Grant Number: RDU170103.

Author information

Authors and Affiliations

  1. Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, 26300, Kuantan, Pahang, Malaysia

    Abdullah B. Nasser & Kamal Z. Zamli

  2. Computer Science Department, Systems and Information Technology Center, Hodeidah University, Hodeidah, Yemen

    Abdullah B. Nasser

Authors
  1. Abdullah B. Nasser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamal Z. Zamli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Abdullah B. Nasser or Kamal Z. Zamli .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. Information Systems Department, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

    Nadhmi Gazem

  3. Information Technology Department, Faculty of Engineering and Information Technology, Taiz University, Taiz, Yemen

    Fathey Mohammed

  4. Information Systems Department, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

    Abdelsalam Busalim

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nasser, A.B., Zamli, K.Z. (2019). Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation. In: Saeed, F., Gazem, N., Mohammed, F., Busalim, A. (eds) Recent Trends in Data Science and Soft Computing. IRICT 2018. Advances in Intelligent Systems and Computing, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-319-99007-1_23

Download citation

Publish with us

Policies and ethics

Search

Navigation