Abstract
The work on mutation testing has attracted a lot of attention during the last decades. Mutation testing is a powerful mechanism to improve the quality of test suites based on the injection of syntactic changes into the code of the original program. Several studies have focused on reducing the high computational cost of applying this technique and increasing its efficiency. Only some of them have tried to do it through the application of genetic algorithms. Genetic algorithms can guide through the generation of a reduced subset of mutants without significant loss of information. In this paper, we analyse recent advances in mutation testing that contribute to reduce the cost associated to this technique and propose to apply them for addressing current drawbacks in Evolutionary Mutation Testing (EMT), a genetic algorithm based technique with promising experimental results so far.
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Acknowledgements
Paper partially funded by the research scholarship PU-EPIF-FPI-PPI-BC 2012-037 (University of Cádiz) and by Spanish government projects DArDOS (TIN2015-65845-C3-3-R (MINECO/FEDER)), SICOMORo-CM (S2013/ICE-3006) and the Excellence Network SEBASENet (TIN2015-71841-REDT (MINECO)).
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Delgado-Pérez, P., Medina-Bulo, I., Merayo, M.G. (2017). Using Evolutionary Computation to Improve Mutation Testing. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_33
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