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GPTSG: A Genetic Programming Test Suite Generator Using Information Theory Measures

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11506))

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Abstract

The automatic generation of test suites that get the best score with respect to a given measure is costly in terms of computational power. In this paper we present a genetic programming approach for generating test suites that get a good enough score for a given measure. We consider a black-box scenario and include different Information Theory measures. Our approach is supported by a tool that will actually generate test suites according to different parameters. We present the results of a small experiment where we used our tool to compare the goodness of different measures.

Research partially supported by the Spanish project DArDOS (TIN2015-65845-C3-1-R) and the Comunidad de Madrid project FORTE-CM (S2018/TCS-4314).

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Notes

  1. 1.

    The tool can be downloaded from https://github.com/Colosu/gptsg.

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Author information

Authors and Affiliations

  1. Complutense University of Madrid, 28040, Madrid, Spain

    Alfredo Ibias & Manuel Núñez

  2. Polytechnic University of Madrid, 28223, Madrid, Spain

    David Griñán

Authors
  1. Alfredo Ibias
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  2. David Griñán
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  3. Manuel Núñez
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Corresponding author

Correspondence to Manuel Núñez .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Ibias, A., Griñán, D., Núñez, M. (2019). GPTSG: A Genetic Programming Test Suite Generator Using Information Theory Measures. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_59

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  • DOI: https://doi.org/10.1007/978-3-030-20521-8_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20520-1

  • Online ISBN: 978-3-030-20521-8

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