Search results
Results from the WOW.Com Content Network
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]
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.
User input validation: User input (gathered by any peripheral such as a keyboard, bio-metric sensor, etc.) is validated by checking if the input provided by the software operators or users meets the domain rules and constraints (such as data type, range, and format).
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]
In computer software, the term parameter validation [1] [2] is the automated processing, in a module, to validate the spelling or accuracy of parameters passed to that module. The term has been in common use for over 30 years. [1] Specific best practices have been developed, for decades, to improve the handling of such parameters. [1] [2] [3]
Naylor and Finger [1967] formulated a three-step approach to model validation that has been widely followed: [1] Step 1. Build a model that has high face validity. Step 2. Validate model assumptions. Step 3. Compare the model input-output transformations to corresponding input-output transformations for the real system. [5]
It verifies that the software functions properly even when it receives invalid or unexpected inputs, thereby establishing the robustness of input validation and error-management routines. [ citation needed ] Software fault injection , in the form of fuzzing , is an example of failure testing.
Defensive programming practices are often used where high availability, safety, or security is needed. Defensive programming is an approach to improve software and source code , in terms of: General quality – reducing the number of software bugs and problems.