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For the follow-up scenario, we assume: Interventions to sensitize doctors to order diagnostic laboratory tests result in an increase by 5-fold in the percentage ordering such tests (input values increase from 10%to50%0%).
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Then, for any new or test input, x ⋆, the method predicts the output as f(x ⋆).
The path partition can be used to design white-box test cases for testing input validation.
Simulation results on various test input sets of intersecting line segments have also been found satisfactory.
Virtual mobile crash reporting system (MCRS) supplies the test input data used as an example.
The ANN model so developed is validated by presenting training and new testing input patterns.
This paper studies the Alternating Variable Method (AVM) approach to search-based test input generation.
However, to test input validation more accurately, a code-based method is also required.
Xie and Zhao [77] discussed existing solutions for AO testing such as test input generation, test selection and runtime checking, mostly developed by the authors.
In this paper, we propose an approach to extract path partition and input conditions from code for testing input validation.
In this paper, the problem of control-relevant test input design for iterative system identification is considered.
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