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The architecture focuses on providing flexibility in terms of testing domain, testing strategy, data storage, and user interface display.
The results indicate that extending UML to the simulation testing domain can effectively and precisely model STSAS.
This architecture is specialized to the software testing domain and the development of tools for that domain is discussed.
High-stakes tests have a crucial role in language teaching and testing domain and serve as a point of emphasis by pioneers in this field.
Within the software testing domain, determining the order in which classes are tested in integration testing is an important problem called class integration test order identification (CITO).
The ability to gracefully handle erroneous inputs is a well-known problem in the robustness testing domain, where tests using invalid inputs applied on external interfaces of many different systems have been used with great success [10 12].
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The workload needs to scale across all testing domains using different processor architectures.
Machine learning algorithms often suffer from good generalization in testing domains especially when the training (source) and test (target) domains do not have similar distributions.
The focus is to hierarchically learn multiple intermediate representations along an interpolating path between the training and testing domains.
These users can also preview feature releases on a test domain.
BDD is largely facilitated through simple domain specific languages (DSL) and usually restricted to technical test domain concepts.
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