Sentence examples for generation of test cases from inspiring English sources

The approach for the generation of test cases proposed here assumes interactive development, analysis and validation of complex vignettes for testing realistic scenarios.

Automated generation of test cases to validate the functionality of the robot trajectory generation unit showcases the key features of this methodology.

All these approaches have in common to a greater or lesser extent the random generation of test cases with the aim of driving the targeted program to an unexpected and possibly exploitable state.

The conclusion of the two evaluations of this second experiment is that our solution is better and quite attractive for the generation of test cases considering higher strengths (5 and 6), where it was superior to basically all other algorithms/tools.

As usually, automatic generation produces a huge amount of test cases that may also have a considerable degree of redundancy (Cartaxo et al. 2008, 2011).

To steer the diffusivity of test case generation, we provide feedback regarding the quality of test cases (as defined in Section 4) to the test generation process in order to yield self-adaptive fuzzing.

The initial seed generation step outputs a non-empty set of test cases (X_{0} subset mathcal {I}), two diffusivity parameters α 1 and α 2, and an initial offset q 0. The inputs X 0 are added to the list of test cases X all.

In the RBAC domain, although very effective and less costly, recent test generation methods still tend to output large amounts of test cases (Damasceno et al. 2016).

In order to dynamically adapt test generation behavior to actual path exploration performance, we define a suitable measure for quality evaluation of test cases.

By instrumenting the binary under test and applying the quality evaluation of test cases introduced in Section 4, we are able to feedback coverage information of currently explored input regions to the test case generation algorithm.

After initial seed generation, the fuzzer enters the loop of test case generation, quality evaluation, adaptation of diffusivity, and test case update.