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Study of an Improved Genetic Algorithm for Multiple Paths Automatic Software Test Case Generation

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Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10385))

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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.

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Acknowledgments

This paper is supported by the National Natural Science Foundation of China (Grant No. 61300169) and the Natural Science Foundation of Education Department of Anhui province (Grant No. KJ2016A257).

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Correspondence to Erzhou Zhu or Feng Liu .

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Zhu, E., Yao, C., Ma, Z., Liu, F. (2017). Study of an Improved Genetic Algorithm for Multiple Paths Automatic Software Test Case Generation. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_44

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  • DOI: https://doi.org/10.1007/978-3-319-61824-1_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61823-4

  • Online ISBN: 978-3-319-61824-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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