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Path-oriented test cases generation based adaptive genetic algorithm

Author

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  • Xiaoan Bao
  • Zijian Xiong
  • Na Zhang
  • Junyan Qian
  • Biao Wu
  • Wei Zhang

Abstract

The automatic generation of test cases oriented paths in an effective manner is a challenging problem for structural testing of software. The use of search-based optimization methods, such as genetic algorithms (GAs), has been proposed to handle this problem. This paper proposes an improved adaptive genetic algorithm (IAGA) for test cases generation by maintaining population diversity. It uses adaptive crossover rate and mutation rate in dynamic adjustment according to the differences between individual similarity and fitness values, which enhances the exploitation of searching global optimum. This novel approach is experimented and tested on a benchmark and six industrial programs. The experimental results confirm that the proposed method is efficient in generating test cases for path coverage.

Suggested Citation

  • Xiaoan Bao & Zijian Xiong & Na Zhang & Junyan Qian & Biao Wu & Wei Zhang, 2017. "Path-oriented test cases generation based adaptive genetic algorithm," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-17, November.
    • Handle: RePEc:plo:pone00:0187471
    DOI: 10.1371/journal.pone.0187471
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    Cited by:

    1. Jeya Mala D. & Ramalakshmi Prabha, 2023. "On the Application of Quick Artificial Bee Colony Algorithm (qABC) for Attenuation of Test Suite in Real-Time Software Applications," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 19(1), pages 1-23, January.

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