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Model-based testing is software testing in which test cases are derived in whole or in part from a model that describes some (usually functional) aspects of the system under test (SUT).


General model-based testing setting. The model is usually an abstract, partial presentation of the system under test's desired behavior. The test cases derived from this model are functional tests on the same level of abstraction as the model. These test cases are collectively known as the abstract test suite. The abstract test suite cannot be directly executed against the system under test because it is on the wrong level of abstraction. Therefore an executable test suite must be derived from the abstract test suite that can communicate with the system under test. This is done by mapping the abstract test cases to concrete test cases suitable for execution. In the case of online testing , the abstract test suite exists only as a concept but not as an explicit artifact.

There are many different ways to "derive" tests from a model. Because testing is usually experimental and based on heuristics, there is no one best way to do this. It is common to consolidate all test derivation related design decisions into a package that is often known as "test requirements", "test purpose" or even "use case". This package can contain e.g. information about the part of the model that should be the focus for testing, or about the conditions where it is correct to stop testing (test stopping criteria).


An example of a model-based testing workflow (offline test case generation). IXIT refers to "implementation extra information" and denotes here the total package of information that is needed when the abstract test suite is converted into an executable one. Typically it includes information about test harness, data mappings and SUT configuration.Because test suites are derived from models and not from source code, model-based testing is usually seen as one form of black-box testing. In some aspects, this is not completely accurate. Model-based testing can be combined with source-code level test coverage measurement, and functional models can be based on existing source code in the first place.

Model-based testing for complex software systems is still an evolving field.
 

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