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Test Data Generation Efficiency Prediction Model for EFSM Based on MGGP

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Book cover Search Based Software Engineering (SSBSE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9962))

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Abstract

Most software testing researches on Extended Finite State Machine (EFSM) have focused on automatic test sequence and data generation. The analysis of test generation efficiency is still inadequate. In order to investigate the relationship between EFSM test data generation efficiency and its influence factors, according to the feasible transition paths of EFSMs, we build a multi-gene genetic programming (MGGP) predictive model to forecast EFSM test data generation efficiency. Besides, considering standard genetic programming (GP) and neural network are commonly employed in predictive models, we conduct experiments to compare MGGP model with GP model and back propagation (BP) neural network model on their predictive ability. The results show that, MGGP model is able to effectively predict EFSM test data generation efficiency, and compared with GP model and BP model, MGGP model’s predictive ability is stronger. Moreover, the correlation among the influence factors will not affect its predictive performance.

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Authors and Affiliations

  1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, People’s Republic of China

    Weiwei Wang, Ruilian Zhao, Ying Shang & Yong Liu

Authors
  1. Weiwei Wang
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  2. Ruilian Zhao
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  3. Ying Shang
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  4. Yong Liu
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Corresponding author

Correspondence to Ruilian Zhao .

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Editors and Affiliations

  1. University College London, London, United Kingdom

    Federica Sarro

  2. Michigan State University , East Lansing, Michigan, USA

    Kalyanmoy Deb

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Wang, W., Zhao, R., Shang, Y., Liu, Y. (2016). Test Data Generation Efficiency Prediction Model for EFSM Based on MGGP. In: Sarro, F., Deb, K. (eds) Search Based Software Engineering. SSBSE 2016. Lecture Notes in Computer Science(), vol 9962. Springer, Cham. https://doi.org/10.1007/978-3-319-47106-8_12

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  • DOI: https://doi.org/10.1007/978-3-319-47106-8_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47105-1

  • Online ISBN: 978-3-319-47106-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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