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Empirical Study of Hybrid Optimization Strategy for Evolutionary Testing

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Data Science (ICPCSEE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1059))

Abstract

Evolutionary testing (ET) is an effective test case generation technique which uses some meta-heuristic search algorithm, especially genetic algorithm, to generate test case automatically. However, the population prematurity problem may decrease the performance of ET. In this paper, a hybrid optimization strategy is proposed based on extended cataclysm which integrates both static configuration strategies and dynamic optimization strategy. Dynamic optimization strategy included the optimization of initial population and the dynamic population optimization based on extended cataclysm, where the diversity of population was monitored during the evolution process of ET, and once the population prematurity was detected, extended cataclysm operation was used to renew the diversity of the population. Experimental results show that the hybrid optimization strategy can improve the performance of ET.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China under Grant No. 61806068, 61672204, by Visiting Scholar at Home and Aboard Funded Project of Universities of Anhui Province under Grant gxfxZD2016209, by Key Technologies R&D Program of Anhui Province under Grant 1804a09020058, by the Major Program for Scientific and Technological of Anhui Province under Grant 17030901026, by Talent Research Foundation Project of Hefei University under Grant 16-17RC23, by Humanities and Social Science Research Project of Universities of Anhui Province under Grant SK2018A0605.

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Authors and Affiliations

  1. Hefei University, No. 99 Jinxiu Avenue, Hefei, Anhui, China

    Chunling Hu, Xiaofeng Wang & Gang Lv

  2. Southeast University, No. 2 Southeast University Road, Nanjing, Jiangsu, China

    Bixin Li

Authors
  1. Chunling Hu
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  2. Bixin Li
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  3. Xiaofeng Wang
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  4. Gang Lv
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Corresponding author

Correspondence to Chunling Hu .

Editor information

Editors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Rui Mao

  2. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  3. Guilin University of Technology, Guilin, China

    Xiaolan Xie

  4. National Academy of Guo Ding Institute of Data Science, Harbin, China

    Zeguang Lu

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Hu, C., Li, B., Wang, X., Lv, G. (2019). Empirical Study of Hybrid Optimization Strategy for Evolutionary Testing. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_3

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  • DOI: https://doi.org/10.1007/978-981-15-0121-0_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0120-3

  • Online ISBN: 978-981-15-0121-0

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