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Classifying Mutants with Decomposition Kernel

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Artificial Intelligence and Soft Computing (ICAISC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9692))

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Abstract

The paper deals with the problem of reducing the cost of mutation testing using artificial intelligence methods. The presented approach is based on the similarity of mutants. The mutants are coded as control flow diagrams representing the programs structure, variables and conditions. The similarity is then calculated with the use of a new graph kernel and used to predict if a given test case detects a mutant or not. The prediction process is performed by a classification algorithm. Experimental results are also presented in this paper on the basis of two systems.

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Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Cracow University of Technology, ul. Warszawska 24, 31-155, Krakow, Poland

    Joanna Strug

  2. Department of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Lojasiewicza 11, 30-059, Krakow, Poland

    Barbara Strug

Authors
  1. Joanna Strug
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  2. Barbara Strug
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Corresponding author

Correspondence to Barbara Strug .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Czestochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Czestochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Czestochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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© 2016 Springer International Publishing Switzerland

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Strug, J., Strug, B. (2016). Classifying Mutants with Decomposition Kernel. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9692. Springer, Cham. https://doi.org/10.1007/978-3-319-39378-0_55

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  • DOI: https://doi.org/10.1007/978-3-319-39378-0_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39377-3

  • Online ISBN: 978-3-319-39378-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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