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If no clinic mutation testing had been performed, the kConFab core research laboratory performed mutation testing.
All strategies except Strategies 1(2) (family history only) and 8 (direct mutation testing) had specificity over 99.5% in relation to probands.
Most obviously, the overview did not include data on genotypes at known susceptibility genes (data that would rarely be available in such studies), so these risk estimates would not apply in families where, for example, BRCA1 or BRCA2 mutation testing had taken place.
BRCA1 and BRCA2 mutation testing had already identified 15 BRCA1 mutation carriers in the family history only group (15 of 150, or 10%), 14 BRCA1 mutation carriers in the morphology only group (14 of 96, or 15%) and 12 BRCA1 mutation carriers in the group who met both criteria (12 of 37, or 32%).
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The mutation testing has been recently explored in the SPL context.
Besides effective, Mutation Testing has some drawbacks, mainly related to the high number of generated mutants.
Mutation testing has been addressed for test data generation only in the work of Henard et al. (2014).
Background: Human immunodeficiency virus type 1 (HIV-1) drug resistance mutation testing has become a useful tool in assessing antiretroviral treatment and managing patient care.
BRCA1/2 mutation testing has become the accepted standard of care in families with a strong history of breast and/or ovarian cancer.
Nevertheless, once mutation testing has confirmed as an effective criterion for test set evaluation (Andrews et al. 2005) we consider the set of faults modeled by its mutation operators a good starting point for FindBugs bug kind prioritization.
Recently, fault-based criteria, such as that ones based on mutation testing, have been investigated for variability test using the FM (Ferreira et al. 2013; Henard et al. 2013a).
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