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The ability of the PALS technique to generalize was evaluated through the generation of test data and statistical simulations.
One more commercial tool dedicated to the generation of test data for Simulink models is Safety Test Builder [18].
In this case, all obtained values show a significance level greater than 95%%, making it possible for us to conclude that the use of randomly generated sets together with the ones generated by the pairwise approach may be the best alternative for the generation of test data according to our experiments.
We focus on the problem of generating test data for many paths coverage, and present a method of evolutionary generation of test data for many paths coverage based on grouping.
Considering path coverage as the test adequacy criterion, we propose using genetic algorithms (GA) for automating the generation of test data for white-box testing.
The system consists of several modules that provide a comprehensive workflow for generation of test data, segmentation method development as well as experiment planning and execution.
Similar(38)
In this experiment we aimed to measure the test data generated using the pairwise approach against a random generation set of test data.
Seeking to address such issue, our main results come from the experimental studies that were conducted to assess the adequacy of test data generation using two different approaches, a pairwise generation [19] and a random one.
In the case of test data generation, as in Zhan and Clark [48] for instance, an initial test set is required in order to apply the optimization algorithms.
We are also not aware of other studies that focus on a well planned and documented experimental evaluation of test data generation methods for Simulink-like models.
The number of test data created is the same as set (RP_1); (Random Random generation as the previous one but the number of test data was taken from the cardinality of (RP_2).
More suggestions(15)
generation of image data
generation of traffic data
generation of test scenarios
generation of test sets
generation of sequence data
generation of test vectors
generation of canSNP data
generation of test inputs
generation of test reactors
generation of network data
generation of microarray data
generation of seroprevalence data
generation of test systems
generation of nutrition data
generation of test signals
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