Quantum­-Inspired Genetic Algorithm for Solving the Test Suite Minimization Problem

Abstract

Test 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. The proposed method has an advantage in that it reduces theassumed minimal number of test cases using quantum measurements, which makes it able to discover the minimalnumber of test cases without any prior assumptions.