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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Study of an Improved Genetic Algorithm for Multiple Paths Automatic Software Test Case Generation
verfasst von : Erzhou Zhu, Chenglong Yao, Zhujuan Ma, Feng Liu
Erschienen in: Advances in Swarm Intelligence
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Abstract
Automatic generation of test case is an important means to improve the efficiency of software testing. As the theoretical and experimental base of the existing heuristic search algorithm, genetic algorithm shows great superiority in test case generation. However, since most of the present fitness functions are designed by a single target path, the efficiency of the generating test case is relatively low. In order to cope with this problem, this paper proposes an efficiency genetic algorithm by using a novel fitness function. By generating multiple test cases to cover multiple target paths, this algorithm needs less iterations hence exhibits higher efficiency comparing to the existing algorithms. The simulation results have also shown that the proposed algorithm is high path coverage and high efficiency.