Test Suite Reduction Based On Modified Quantum Ant Colony Algorithm

Test suite reduction is find a subset based on some algorithms with some restriction from original test suite which not only contains less numbers of test cases and cost least when execute than original test suite but also cover the same test requests as original test suite.Researchers proposed many algorithms to solve this problem,for examples Greedy algorithm,GRE algorithm,HGS algorithm,and Linear Programing algorithm but these algorithms are based on a theory which consider that execute each test case cost same sources,test suite reduction is find a subset which contains least test cases,but the result maybe not minimize the total execute cost.In this pepper we want to find a subset based on our algorithm which not contains least cases but a subset which cost minimum.After long time analyze we present a modified quantum ant colony algorithm.We present a new algorithm which is based on quantum ant colony algorithm,which is united the quantum evolutionary algorithm into ant colony to improve the effective and performance of ant colony system.As the scale of original test suite is rather big and reduce the calculation scale of modified quantum ant colony algorithm we use modified greedy algorithm first to get a candidate set.To enlarge the hunting zone of ants and accelerate the convergence of ant colony self-adaptive strategy was used in parameters setting.At last we enhanced pheromone of each node at the beginning of ant colony hunts,global pheromone update and local pheromone update are both united to update pheromone of each node,to avoid ant colony trapped into stagnation pheromone of each node limited in section.Finally we make quantity of simulation experiments based on Siemens standard test suite,then compared the result with other algorithms,we find that the modified quantum ant colony system can solve the problem more efficient and perform better.