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Genetic and Evolutionary Computing pp 455–463Cite as

Test Path Optimization Algorithm Compared with GA Based Approach

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 387))

Abstract

Software testing performs a vital and basic role in developing software that is freedom from bugs and defects. Various levels of testing use Unified modeling language (UML) to design the tests. The test case generation from design specification is an essential work in testing phase. In recent trend, Model-Based testing approach attracts many researchers by using some search optimization techniques to produce an automated optimal test path. The best test path is got more beneficial than the other generated test paths because it can reduce time and cost more than others. In this paper, we compare and analysis different methodologies of model based best test path generation by using the UML activity diagram. We discuss the results the resulting test paths with respect to correctness and time consuming.

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References

  1. http://www.softwareresults.us/defects-in-software.html

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Author information

Authors and Affiliations

  1. University of Computer Studies, Mandalay, Myanmar

    Aye Aye Kyaw & Myat Myat Min

Authors
  1. Aye Aye Kyaw
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  2. Myat Myat Min
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Corresponding author

Correspondence to Aye Aye Kyaw .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Thi Thi Zin

  2. School of Computer Science and Tech..., Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Jerry Chun-Wei Lin

  3. College of Information Science and Engg, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

  4. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Pyke Tin

  5. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Mitsuhiro Yokota

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Kyaw, A.A., Min, M.M. (2016). Test Path Optimization Algorithm Compared with GA Based Approach. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-319-23204-1_46

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  • DOI: https://doi.org/10.1007/978-3-319-23204-1_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23203-4

  • Online ISBN: 978-3-319-23204-1

  • eBook Packages: EngineeringEngineering (R0)

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