038b55c8-95a7-4c8a-a03d-51d4f7e039f220210316054714080wseamdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON COMPUTERS1109-275010.37394/23205http://wseas.org/wseas/cms.action?id=40262720202720201910.37394/23205.2020.19http://wseas.org/wseas/cms.action?id=23186Quantum-Inspired Genetic Algorithm for Solving the Test Suite Minimization ProblemHagerHusseinDepartment of Software Engineering, College of Computing and Information Technology, Arab Academy for Science and Technology, Alexandria, EGYPTAhmedYounesDepartment of Mathematics and Computer Science, Faculty of Science, Alexandria University, Alexandria, EGYPT, also with School of Computer Science, University of Birmingham, Birmingham, UNITED KINGDOMWalidAbdelmoezDepartment of Software Engineering, College of Computing and Information Technology, Arab Academy for Science and Technology, Alexandria, EGYPTTest Suite Minimization problem is a nondeterministic polynomial time (NP) complete problem insoftware engineering that has a special importance in software testing. In this problem, a subset with a minimalsize that contains a number of test cases that cover all the test requirements should be found. A bruteforceapproach to solving this problem is to assume a size for the minimal subset and then search to find if there is asubset of test cases with the assumed size that solves the problem. If not, the assumed minimal size is graduallyincremented, and the search is repeated. In this paper, a quantuminspired genetic algorithm (QIGA) will beproposed to solve this problem. In it, quantum superposition, quantum rotation and quantum measurement willbe used in an evolutionary algorithm. The paper will show that the adopted quantum techniques can speed upthe convergence of the classical genetic algorithm. 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