Multi-objective test suite minimisation using Quantum-inspired Multi-objective Differential Evolution Algorithm

• Published in: 2012 IEEE International Conference on Computational Intelligence and Computing Research pp. 1 - 7
• Main Authors: Kumari, A. Charan, Srinivas, K., Gupta, M. P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2012
• Subjects: Codes; Evolutionary computation; Linear programming; Minimization; Optimization; Pareto optimization; Qubit; regression testing; Software; software testing; test suite minimisation; Testing; Vectors
• ISBN: 1467313424; 9781467313421
• DOI: 10.1109/ICCIC.2012.6510272

This paper presents the solution for multi-objective test suite minimisation problem using Quantum-inspired Multi-objective differential Evolution Algorithm. Multi-objective test suite minimisation problem is to select a set of test cases from the available test suite while optimizing the multi objectives like code coverage, cost and fault history. As test suite minimisation problem is an instance of minimal hitting set problem which is NP-complete; it cannot be solved efficiently using traditional optimization techniques especially for the large problem instances. This paper presents Quantum-inspired Multi-objective Differential Evolution Algorithm (QMDEA) for the solution of multi-objective test suite minimisation problem. QMDEA combines the preeminent features of Differential Evolution and Quantum Computing. The features of QMDEA help in achieving quality Pareto-optimal front solutions with faster convergence. The performance of QMDEA is tested on two real world applications and the results are compared against the state-of-the-art multi-objective evolutionary algorithm NSGA-II. The comparison of the obtained results indicates superior performance of QMDEA.