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Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
Specificity (true negative rate) is the probability of a negative test result, conditioned on the individual truly being negative. If the true status of the condition cannot be known, sensitivity and specificity can be defined relative to a " gold standard test " which is assumed correct.
Independent Software Verification and Validation (ISVV) is targeted at safety-critical software systems and aims to increase the quality of software products, thereby reducing risks and costs throughout the operational life of the software. The goal of ISVV is to provide assurance that software performs to the specified level of confidence and ...
Model validation is defined to mean "substantiation that a computerized model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model". [3] A model should be built for a specific purpose or set of objectives and its validity determined for that purpose. [3]
A broad definition of verification makes it related to software testing. In that case, there are two fundamental approaches to verification: Dynamic verification, also known as experimentation, dynamic testing or, simply testing. - This is good for finding faults (software bugs).
Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
Inspection is a verification method that is used to compare how correctly the conceptual model matches the executable model. Teams of experts, developers, and testers will thoroughly scan the content (algorithms, programming code, documents, equations) in the original conceptual model and compare with the appropriate counterpart to verify how closely the executable model matches. [1]
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]