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Cost Reduction in Mutation Testing with Bytecode-Level Mutants Classification

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10841))

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Abstract

The paper presents the application of classification based approach to software quality domain. In particular it deals with the issue of reducing the cost of mutation testing. The presented approach is based on the similarity of mutants represented at the bytecode level. The distance matrix for mutants is used in kNN algorithm to predict if a given test set detects a mutant or not. Experimental results are also presented in this paper on the basis of two systems. The obtained results show the usefulness of the proposed method.

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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, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Strug, J., Strug, B. (2018). Cost Reduction in Mutation Testing with Bytecode-Level Mutants Classification. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_66

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  • DOI: https://doi.org/10.1007/978-3-319-91253-0_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91252-3

  • Online ISBN: 978-3-319-91253-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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