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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Combining Genetic Algorithms and Mutation Testing to Generate Test Sequences
verfasst von : Carlos Molinero, Manuel Núñez, César Andrés
Erschienen in: Bio-Inspired Systems: Computational and Ambient Intelligence
Verlag: Springer Berlin Heidelberg
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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.