Cost-effective test case generation with the hyper-heuristic for software product line testing
Implementation of optimization algorithm for test case generation in Model-Based Testing (MBT) for Software Product Line (SPL) has been increasing, due to the demand for optimal test case results with a balanced trade-off between cost and effectiveness measure. This paper proposed a hyper-heuristic...
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| Main Authors: | , , |
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| Format: | Article |
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Elsevier Ltd
2023
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| Subjects: | |
| Online Access: | http://eprints.utm.my/104881/ http://dx.doi.org/10.1016/j.advengsoft.2022.103335 |
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| Summary: | Implementation of optimization algorithm for test case generation in Model-Based Testing (MBT) for Software Product Line (SPL) has been increasing, due to the demand for optimal test case results with a balanced trade-off between cost and effectiveness measure. This paper proposed a hyper-heuristic test cases generation approach in MBT for SPL called Improvement Selection Rules-Modified Choice Function (ISR-MCF). ISR-MCF is implemented with three search operators which are Non-Dominated Sorting Genetic Algorithm II with low-level heuristic (NSGA-II-LLH), Strength Pareto Evolutionary with Low-Level Heuristic (SPEA 2-LLH) and Particle Swarm Optimization with Low-Level Heuristic (PSO-LLH). The approach was evaluated with a test model and the result shows that the proposed ISR-MCF with NSGA-II-LLH outperforms other existing rules for minimization measure (size of a test suite and execution time and maximization measure (coverage criteria). |
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