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Neural Computing and Applications

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Published in: Neural Computing and Applications 34/2023

18-09-2023 | Original Article

Q-learning whale optimization algorithm for test suite generation with constraints support

Authors: Ali Abdullah Hassan, Salwani Abdullah, Kamal Z. Zamli, Rozilawati Razali

Published in: Neural Computing and Applications | Issue 34/2023

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Abstract

This paper introduces a new variant of a metaheuristic algorithm based on the whale optimization algorithm (WOA), the Q-learning algorithm and the Exponential Monte Carlo Acceptance Probability called (QWOA-EMC). Unlike WOA, QWOA-EMC permits just-in-time adaptive selection of its operators (i.e., between shrinking mechanism, spiral shape mechanism, and random generation) based on their historical performances as well as exploits the Monte Carlo Acceptance probability to further strengthen its exploration capabilities by allowing a poor performing operator to be reselected with probability in the early part of the iteration. Experimental results for constraints combinatorial test generation demonstrate that the proposed QWOA-EMC outperforms WOA and performs competitively against other metaheuristic algorithms.
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Literature
1.
Hassan AA, Abdullah S, Zamli KZ, Razali R (2020) Combinatorial test suites generation strategy utilizing the whale optimization algorithm. IEEE Access 8:192288–192303 CrossRef
2.
Abdullah S, Sabar NR, Nazri MZA, Ayob M (2014) An exponential Monte-Carlo algorithm for feature selection problems. Comput Ind Eng 67:160–167 CrossRef
3.
Sabar, N. R., Ayob, M., & Kendall, G. (2009). Tabu exponential monte-carlo with counter heuristic for examination timetabling. In 2009 IEEE Symposium on Computational Intelligence in Scheduling (pp. 90-94). IEEE.
4.
5.
6.
Behmanesh R, Rahimi I, Gandomi AH (2021) Evolutionary many-objective algorithms for combinatorial optimization problems: a comparative study. Arch Comput Meth Eng 28(2):673–688 MathSciNetCrossRef
7.
Kassaymeh S, Abdullah S, Al-Laham M, Alweshah M, Al-Betar MA, Othman Z (2021) Salp swarm optimizer for modeling software reliability prediction problems. Neural Process Lett 53(6):4451–4487 CrossRef
8.
Muazu AA, Hashim AS, Sarlan A (2022) Review of nature inspired metaheuristic algorithm selection for combinatorial t-way testing. IEEE Access 10:27404–27431 CrossRef
9.
Esfandyari S, Rafe V (2018) A tuned version of genetic algorithm for efficient test suite generation in interactive t-way testing strategy. Inf Softw Technol 94:165–185 CrossRef
10.
Sabharwal S, Bansal P, Mittal N, Malik S (2016) Construction of mixed covering arrays for pair-wise testing using probabilistic approach in genetic algorithm. Arab J Sci Eng 41(8):2821–2835 CrossRef
11.
Bansal P, Sabharwal S, Mittal N, Arora S (2015) Construction of variable strength covering array for combinatorial testing using a greedy approach to genetic algorithm, e-Inf Softw Eng J 9(1)
12.
Wu H, Nie C, Kuo F-C, Leung H, Colbourn CJ (2014) A discrete particle swarm optimization for covering array generation. IEEE Trans Evolut Comput 19(4):575–591 CrossRef
13.
Ahmed BS, Abdulsamad TS, Potrus MY (2015) Achievement of minimized combinatorial test suite for configuration-aware software functional testing using the cuckoo search algorithm. Inf Softw Technol 66:13–29 CrossRef
14.
Alsewari ARA, Zamli KZ (2012) Design and implementation of a harmony-search-based variable-strength t-way testing strategy with constraints support. Inf Softw Technol 54(6):553–568 CrossRef
15.
Zamli KZ, Din F, Ahmed BS, Bures M (2018) A hybrid q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem. PloS One 13(5):e0195675 CrossRef
16.
Mahmoud T, Ahmed BS (2015) An efficient strategy for covering array construction with fuzzy logic-based adaptive swarm optimization for software testing use. Exp Syst Appl 42(22):8753–8765 CrossRef
17.
Zamli KZ, Ahmed BS, Mahmoud T, Afzal W (2018) Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation, arXiv preprint arXiv:​1810.​05824
18.
Zamli KZ, Din F, Baharom S, Ahmed BS (2017) 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 CrossRef
19.
Nasser AB, Zamli KZ, Ahmed BS (2019) Dynamic solution probability acceptance within the flower pollination algorithm for t-way test suite generation, arXiv preprint arXiv:​1902.​11160
20.
Htay KM, Othman RR, Amir A, Alkanaani JMH (2021) Gravitational search algorithm based strategy for combinatorial t-way test suite generation. J King Saud Univ Comput Inf Sci 34(8):4860–4873
21.
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evolut Comput 1(1):67–82 CrossRef
22.
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67 CrossRef
23.
24.
Samma H, Lim CP, Saleh JM (2016) A new reinforcement learning-based memetic particle swarm optimizer. Appl Soft Comput 43:276–297 CrossRef
25.
Alazzawi AK, Rais HM, Basri S (2019) Abcvs: an artificial bee colony for generating variable t-way test sets. Int J Adv Comput Sci Appl
26.
Jenkins B (2016) Jenny test tool. http://​www.​burtleburtle.​net.​/​bob/​math/​jenny.​html
27.
Williams AW (2000) Determination of test configurations for pair-wise interaction coverage. In: Testing of communicating systems, Springer, pp 59–74
28.
Czerwonka J, Butt D, Gens C (2006) Pairwise testing in real word: practical extensions to test case generators. In: Proceedings of the 24th pacific northwest software quality conf, Vol. 2006
29.
Lei Y, Kacker R, Kuhn DR, Okun V, Lawrence J (2008) Ipog/ipog-d: efficient test generation for multi-way combinatorial testing. Softw Test Verif Reliab 18(3):125–148 CrossRef
30.
Lei Y, Kacker R, Kuhn DR, 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), IEEE, pp 549–556
31.
Torres-Jimenez J, Perez-Torres JC (2019) A greedy algorithm to construct covering arrays using a graph representation. Inf Sci 477:234–245 MathSciNetCrossRefMATH
32.
Alsewari A, Zamli KZ, Al-Kazemi B (2015) Generating t-way test suite in the presence of constraints. J Eng Technol (JET) 6(2):52–66
33.
Cohen MB, Dwyer MB, 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
34.
Alsariera YA, Ahmed HAS, Alamri HS, Majid MA, Zamli KZ (2018) A bat-inspired testing strategy for generating constraints pairwise test suite. Adv Sci Lett 24(10):7245–7250 CrossRef
35.
Alazzawi AK, Rais HM, Basri S, Alsariera YA (2019) Phabc: a hybrid artificial bee colony strategy for pairwise test suite generation with constraints support. In: 2019 IEEE student conference on research and development (SCOReD), IEEE, pp 106–111
36.
Zamli KZ, Klaib MF, Younis MI, Isa NAM, Abdullah R (2011) Design and implementation of a t-way test data generation strategy with automated execution tool support. Inf Sci 181(9):1741–1758 CrossRef
37.
Vlad-Roubtsov, Emma: a free java code coverage tool (2006). http://​emma.​sourceforge.​net
38.
Altawallbeh Z, Al-Smadi M, Komashynska I, Ateiwi A (2018) Numerical solutions of fractional systems of two-point bvps by using the iterative reproducing kernel algorithm. Ukrain Math J 70(5):687–701 MathSciNetCrossRefMATH
Metadata
Title
Q-learning whale optimization algorithm for test suite generation with constraints support
Authors
Ali Abdullah Hassan
Salwani Abdullah
Kamal Z. Zamli
Rozilawati Razali
Publication date
18-09-2023
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 34/2023
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-023-09000-2

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