Abstract: A method for testing software applications in a system under test (SUT) includes building a reference model of the SUT that defines a computer-based neural network. The method includes training the reference model using input data and corresponding output data generated by the SUT, selecting an output value within a domain of possible output values of the SUT representing an output that is not represented in the output data used to train the reference model, applying the selected output value to the reference model, and tracing the selected output through the reference model to identify test input values that when input to the reference model, produce the selected output value. The method can further include using the identified test input values to test the system under test.

Abstract: A method for testing software applications in a system under test (SUT) includes building a reference model of the SUT comprising a computer-based neural network, training the reference model using input data and corresponding output data generated by the SUT, selecting an output value within a domain of possible output values of the SUT representing an output that is not represented in the output data used to train the reference model, applying the selected output value to the reference model and tracing the selected output through the reference model to identify test input values that when input to the reference model, produce the selected output value and using the identified test input values to test the system under test.

Abstract: Systems and methods are described that dynamically reprioritize test cases for Model-Based Testing (MBT) during test execution. Test case execution is prioritized according to their potential to detect uncovered failures within a design model.

Abstract: Systems and methods are described that dynamically reprioritize test cases for Model-Based Testing (MBT) during test execution. Test case execution is prioritized according to their potential to detect uncovered failures within a design model.

Abstract: Product line engineering testing is provided by segmenting a workflow into variable and common activity areas. A workflow decision node can be generated to isolate the segmented variable area, and a stub activity is generated and substituted into the workflow in place of the segmented variable activities. The stub activity can be configured to generate valid output for the substituted variable activities, and can be configured for black-box, gray-box, and white-box behavior.