A Hybrid Test Generation Approach Based on Extended Finite State Machines

Ţurlea, Ana; Ipate, Florentin; Lefticaru, Raluca

doi:10.1109/synasc.2016.037

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…In the second step, for each path, the input values for parameters occurring in guards and triggering each transition of the path are obtained. [26] generates test data for EFSMs using a hybrid genetic algorithm, which is an improvement from the approach presented in [25]. In these approaches, the test data generation problem is converted to an optimisation problem.…”

Section: Definitionmentioning

confidence: 99%

“…-the new population := the offspring population [25]; -the new population := the offspring population and the fittest individual is kept in the next generation [26]; -apply the selection operator and select N chromosomes from the offspring population along with the old population, using different selectors: best chromosome selection, binary tournament selection.…”

Section: Algorithm Configurationmentioning

confidence: 99%

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Search-based testing in membrane computing

et al. 2019

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Search-based testing is widely used for generating test sets. It is also applied in the case of model-based testing, especially for (extended) finite state machines. In this paper, we define such an approach for kernel P system models. We consider a specific kernel P system model and a define a search-based testing method. The test set generated consists of input sequences producing a given computation defined by the model. An example illustrates the use of the introduced method.

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Section: Definitionmentioning

confidence: 99%

Section: Algorithm Configurationmentioning

confidence: 99%