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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]
The following Python code outlines a function which will return the initial CRC remainder for a chosen input and polynomial, with either 1 or 0 as the initial padding. Note that this code works with string inputs rather than raw numbers:
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]
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).
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]
Therefore, systems that pad to a specific number of digits (by converting 1234 to 0001234 for instance) can perform Luhn validation before or after the padding and achieve the same result. The algorithm appeared in a United States Patent [ 1 ] for a simple, hand-held, mechanical device for computing the checksum.
Specification-based testing aims to test the functionality of software according to the applicable requirements. [2] This level of testing usually requires thorough test cases to be provided to the tester, who then can simply verify that for a given input, the output value (or behavior), either "is" or "is not" the same as the expected value specified in the test case.
Example 1: legacy code may have been designed for ASCII input but now the input is UTF-8. Example 2 : legacy code may have been compiled and tested on 32-bit architectures, but when compiled on 64-bit architectures, new arithmetic problems may occur (e.g., invalid signedness tests, invalid type casts, etc.).