Need to write a blog regarding the following requirements and questions.
Model-based testing is a testing technique where test cases can be manually or automatically generated from application models. Models are abstractions of the real-world functionality which exhibit the expected behavior of the system under test. More complex software systems are generally modeled by UML sequence diagrams, source code, etc. The source code and UML diagrams can be modeled in a abstractions state transition machines/diagrams, petri nets, data flow diagrams, etc
Once the models are ready, automated validation tools can be used to validate different permutations and combinations of the functions and data flows. These automated validation tools generate test cases from the models automatically. Some of these tools can automatically generate test cases from models written in Python language. When used properly this approach can also be used to enhance overall test coverage. Some of these tools like Graphwalker, an open-source model-based testing tool, can be used to create test cases automatically. This tool simplifies the creation of test cases by using graphs. Based on the graphs, it generates a path that is used to design test cases. TOSCA is a commercial tool that also supports model-based testing.
A key advantage of model-based testing is the optimization of software testing time and cost. These tools determine the shortest possible path from a start point to an endpoint. The ability to generate test cases automatically driving efficiency in the overall testing approach, and also less error-prone. Model-based testing also generates a minimal number of test cases to validate a given functional or data flow. This ensures higher levels of test coverage, resulting in cost optimization.
What are some of the models that would be used for model based testing?
For these models, how can test cases be generated and how can output be verified?
How would model based testing be integrated into a holisitic test program?

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