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Model-based testing is an application of model-based design for designing and optionally also executing artifacts to perform software testing or system testing. Models can be used to represent the desired behavior of a system under test (SUT), or to represent testing strategies and a test environment.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
The testers are only aware of what the software is supposed to do, not how it does it. [2] Black-box testing methods include: equivalence partitioning, boundary value analysis, all-pairs testing, state transition tables, decision table testing, fuzz testing, model-based testing, use case testing, exploratory testing and specification-based testing.
Analytical Performance Modeling is a method to model the behaviour of a system in a spreadsheet. It is used in Software performance testing . It allows evaluation of design options and system sizing based on actual or anticipated business usage.
A second limitation is the inability to model strategies that would affect historic prices. Finally, backtesting, like other modeling, is limited by potential overfitting . That is, it is often possible to find a strategy that would have worked well in the past, but will not work well in the future. [ 1 ]
Contractual acceptance testing is performed based on the contract's acceptance criteria defined during the agreement of the contract, while regulatory acceptance testing is performed based on the relevant regulations to the software product. Both of these two tests can be performed by users or independent testers.
Classic profile-based prediction worked well for early single-issue, in-order execution processors, but fails to accurately predict the performance of modern processors. The major reason is that modern processors can issue and execute several instructions at the same time, sometimes out of the original order and cross the boundary of basic blocks.
Reliability growth based prediction This method uses documentation of the testing procedure. For example, consider a developed software and that we are creating different new versions of that software. We consider data on the testing of each version and based on the observed trend, we predict the reliability of the new version of software. [13]