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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]
In the context of hardware and software systems, formal verification is the act of proving or disproving the correctness of a system with respect to a certain formal specification or property, using formal methods of mathematics. [1] Formal verification is a key incentive for formal specification of systems, and is at the core of formal methods.
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.
A validator is a computer program used to check the validity or syntactical correctness of a fragment of code or document. The term is commonly used in the context of validating HTML, [1] [2] CSS, and XML documents like RSS feeds, though it can be used for any defined format or language.
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]
Digest access authentication is one of the agreed-upon methods a web server can use to negotiate credentials, such as username or password, with a user's web browser.This can be used to confirm the identity of a user before sending sensitive information, such as online banking transaction history.
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]
This section gives examples of diagnostics generated by the markup validation service, and suggests possible fixes. Diagnostics are by line and column of the generated HTML for the page. It may be helpful to obtain the HTML in order to understand the diagnostic. For example, if you using the Firefox browser, you can type control-U to see the HTML.