Abstract.Tobias is a combinatorial test generation tool which can efficiently generate a large number of test cases by unfolding a test pattern and computing all combinations of parameters. In this paper, we first propose a model-based testing approach where Tobias test cases are first run on an executable UML/OCL specification. This animation of test cases on a model allows to filter out invalid test sequences produced by blind enumeration, typically the ones which violate the pre-conditions of operations, and to provide an oracle for the valid ones. We then introduce recent extensions of the Tobias tool which support an incremental unfolding and filtering process, and its associated toolset. This allows to address explosive test patterns featuring a large number of invalid test cases, and only a small number of valid ones. For instance, these new constructs could mandate test cases to satisfy a given predicate at some point or to follow a given behavior. The early detection of invalid test cases improves the calculation time of the whole generation and execution process, and helps fighting combinatorial explosion.
International audienceIn this paper, we present a model-based testing tool resulting from a research project, named TASCCC. This tool is a complete tool chain dedicated to property-based testing in UML/OCL, that integrates various technologies inside a dedicated Eclipse plug-in. The test properties are expressed in a dedicated language based on property patterns. These properties are then used for two purposes. First, they can be employed to evaluate the relevance of a test suite according to specific coverage criteria. Second, it is possible to generate test scenarios that will illustrate or exercise the property. These test scenarios are then unfolded and animated on the Smartesting's Certify It model animator, that is used to filter out infeasible sequences. This tool has been used in industrial partnership, aiming at providing an assistance for Common Criteria evaluations, especially by providing test generation reports used to show the link between the test cases and the Common Criteria artefacts
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.