Abstract
The goal of this paper is to provide a method to generate efficient and short test suites for Finite State Machines (FSMs) by means of combining Genetic Algorithms (GAs) techniques and mutation testing. In our framework, mutation testing is used in various ways. First, we use it to produce (faulty) systems for the GAs to learn. Second, it is used to sort the intermediate tests with respect to the number of mutants killed. Finally, it is used to measure the fitness of our tests, therefore allowing to reduce redundancy. We present an experiment to show how our approach outperforms other approaches.
Research supported by the Spanish projects WEST (TIN2006-15578-C02-01) and MATES (CCG08-UCM/TIC-4124).
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Molinero, C., Núñez, M., Andrés, C. (2009). Combining Genetic Algorithms and Mutation Testing to Generate Test Sequences. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_43
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DOI: https://doi.org/10.1007/978-3-642-02478-8_43
Publisher Name: Springer, Berlin, Heidelberg
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