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- W2946142852 abstract "One approach widely adopted for software testing rely on mutation to use as their comparison criteria. However, in mutation testing, the number of useless mutants generated during the mutation process can reach up to 90% of the total number of mutants created, making the process costly and time-consuming. The concept of subsumption has been empirically used in mutation testing to establish fault hierarchies in which tests that detect certain failures guarantee the detection of others, drastically reducing the generation of these useless mutants during mutation analysis. The tools used for mutation testing do not yet present automated solutions for checking for dominance and, consequently, selection of mutants to be considered in the tests. Hence, in this paper we present a model of automatic identification of subsumption in mutation testing based on evolutionary algorithms, demonstrating evidence of the efficiency of the use of artificial computational approaches for automatic generation of graphs of dynamic dominances in complex problems. We consider in our approach the dynamic subsumption method and a visualization model from the generation of graphs.We show that our approach is feasible and useful." @default.
- W2946142852 created "2019-05-29" @default.
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- W2946142852 date "2019-01-01" @default.
- W2946142852 modified "2023-10-17" @default.
- W2946142852 title "Subsumption in Mutation Testing: An Automated Model Based on Genetic Algorithm" @default.
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- W2946142852 doi "https://doi.org/10.1007/978-3-030-14070-0_24" @default.
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