Cost-effective test case generation with the hyper-heuristic for software product line testing. (January 2023)

• Record Type: Journal Article
• Authors: Sulaiman, Rabatul．Aduni; Jawawi, Dayang．N.A.; Halim, Shahliza．Abdul
• Is Part Of: Advances in engineering software. Volume 175(2023)
• Publication Date: 2023-01
• Subjects: Software testing -- Optimization algorithm -- Software product line -- Cost effectiveness measure; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103335
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: In Software Product Line, the demand is to have a trade-off between cost and effectiveness measure in test case generation. Cost-effectiveness measure in test case generation can be achieved using hyper-heuristic approach. Hyper-heuristic approach helps in optimization which are to solve maximization and minimization problem. Abstract: 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).