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Model-Based Testing cuts through the noise of performance testing across multi-tier architecture, automatically generating the smallest set of tests and data that “cover” every distinct combination of data and API call. With Test Modeller, the automated tests can be executed across a range of frameworks, enabling rigorous functional and performance testing from centrally maintained models.
Watch this example of web testing an eCommerce stores’ UI and API layer to see how complete performance tests can be generated for the Taurus automation framework, with results analysed in BlazeMeter. You will see how:
1. Complete functional models of the system under test can be built rapidly in-sprint, mapping the logical journeys users can through a complex system’s multi-tier architecture
2.Test data to simulate every real-world performance scenario can be defined using over 500 easy-to-use functions that resolve “just in time” during test execution.
3. The complete test data can be rolled-up into complete JSON messages, defined and compiled using a simple, visual builder.
4. Parameterised tests are built using a simple “fill-in-the-blanks” methodology, where new actions and objects can be made available from a range of frameworks like Taurus, LoadRunner and JMeter.
5. Automated coverage algorithms generate the smallest set of automated performance tests needed to cover every logically distinct combination of test data and API call.
6. Performance tests are executed automatically using VIP Robotic Process Automation, with run results updated in Test Modeller, BlazeMeter, and across Continuous Deliver and ALM tools.
7. Subflows make testing complex chains of API calls easy – simply drag-and-drop blocks together to generate automated performance tests and complete test data.
8. Test maintenance is replaced by updating easy-to-use, central models, re-generating a new set of automated tests and data.
