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To allow for the fact that not all mutations could be detected by the screening methods used, we allowed in our analysis for a sensitivity of mutation testing parameter, giving the probability of detecting a mutation if one exists.
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The values of each of these parameters were mapped into three discrete values, as shown in Table 3. Table 3 GA testing parameter values Parameter 1 2 3 A: crossover rate 0.55 0.675 0.95 B: mutation rate 0.005 0.0075 0.01 C: population size 50 751000 D: maximum generations 25 50 75 The Taguchi method requires translation of the discrete variable values onto a unified scale.
These parameters should be discussed with the pathologists and laboratory staff prior to RAS mutation testing.
According to section 'Fault-based testing and mutation testing', mutation testing [41] is a largely explored fault-based criterion.
This paper describes a new approach to mutation testing, called semantic mutation testing.
Mutation testing is a powerful and flexible test technique.
Recently, we investigated the cost reduction of mutation testing based on the identification of sufficient mutation operators [28].
In order to reduce the cost of Mutation Testing, Table 9 shows the mutation operators from μJava applied incrementally.
For example, mutation testing was combined with ACO to generate a test-input space [30].
The mutation testing can be an alternative for the testing of Simulink-like models.
We had applied the mutation testing as a mean to evaluate our test data generation approaches.
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