Search results
Results from the WOW.Com Content Network
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.
This is a list of models and meshes commonly used in 3D computer graphics for testing and demonstrating rendering algorithms and visual effects. Their use is important for comparing results, similar to the way standard test images are used in image processing.
Test oracles are most commonly based on specifications and documentation. [13] [14] A formal specification used as input to model-based design and model-based testing would be an example of a specified test oracle. The model-based oracle uses the same model to generate and verify system behavior. [15]
Metamorphic testing (MT) is a property-based software testing technique, which can be an effective approach for addressing the test oracle problem and test case generation problem. The test oracle problem is the difficulty of determining the expected outcomes of selected test cases or to determine whether the actual outputs agree with the ...
test cases for the coverage of Simulink models by using static analysis and a search-based method [7] test cases by building sequence from variants of states and transitions of a test model; test cases by transforming recordings of user interactions with the system under test via a graphical user interface (Dashboard)
The Test Template Framework (TTF) is a model-based testing (MBT) framework proposed by Phil Stocks and David Carrington in (Stocks & Carrington 1996) for the purpose of software testing. Although the TTF was meant to be notation-independent, the original presentation was made using the Z formal notation .
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 validation test consists of comparing outputs from the system under consideration to model outputs for the same set of input conditions. Data recorded while observing the system must be available in order to perform this test. [3] The model output that is of primary interest should be used as the measure of performance. [1]