Systematic testing of software plays an important role in the quest for improved software quality. Testing, however, turns out to be an error-prone, expensive, and time-consuming process. Model-based testing is one of the promising technologies to meet the challenges imposed on software testing. With model-based testing the system under test is tested against a model of the system's behaviour. Such a model serves as a precise specification of what the system under test shall do, and, consequently, is a good basis for the automatic generation of test cases and the analysis of test results. This allows effective test automation beyond the current testing practice of automatic execution of manually written test scripts. And if the model is valid, then all generated test cases are provably valid too.

In this presentation, an overview of model-based testing is given, ranging from general concepts, via theory and tools, to academic and industrial usage. Benefits and issues of model-based testing and the relation to other model-driven activities are discussed. A specific theory for model-based testing using transition system models, including a conformance relation and a test generation algorithm, is presented. Corresponding tools and some (industrial) applications will be shown. Altogether these demonstrate that model-based testing is more than just the arbitrary generation of some test cases from a model: a well-defined theory of model-based testing turns out to be both necessary and practical.