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