Abstract
Software testing aims at exploring faults within software in order to ensure it meets all necessary specifications. Test case design strategies play key role in software testing. Classical test case design strategies, however, do not sufficiently include support for exploration of faults due to interaction between parameter values. New strategies known as t-way strategies (where t expresses interaction strength) have been developed for finding interaction faults. However, existing t-way strategies for input-output-based relationship (IOR) interaction testing mostly adopt greedy algorithms which often generate poor quality test data. Therefore, this paper presents the design of a new IOR test suite generation strategy called IOR_HH based on the exponential Monte Carlo with counter (EMCQ) hyper-heuristic. EMCQ is a parameter free hyper-heuristic which works as controller of the three implemented low-level meta-heuristic operators, namely crossover, peer learning and global pollination in the proposed IOR_HH strategy. Experimental results demonstrate the impact of the proposed strategy against existing computational strategies for IOR interaction testing.
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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.
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Din, F., Zamli, K.Z. (2022). Hyper-Heuristic Strategy for Input-Output-Based Interaction Testing. In: Ab. Nasir, A.F., Ibrahim, A.N., Ishak, I., Mat Yahya, N., Zakaria, M.A., P. P. Abdul Majeed, A. (eds) Recent Trends in Mechatronics Towards Industry 4.0. Lecture Notes in Electrical Engineering, vol 730. Springer, Singapore. https://doi.org/10.1007/978-981-33-4597-3_88
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