Abstract
The paper presents the application of classification based approach to software quality domain. In particular it deals with the issue of reducing the cost of mutation testing. The presented approach is based on the similarity of mutants represented at the bytecode level. The distance matrix for mutants is used in kNN algorithm to predict if a given test set detects a mutant or not. Experimental results are also presented in this paper on the basis of two systems. The obtained results show the usefulness of the proposed method.
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Strug, J., Strug, B. (2018). Cost Reduction in Mutation Testing with Bytecode-Level Mutants Classification. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_66
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DOI: https://doi.org/10.1007/978-3-319-91253-0_66
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