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Validating tool

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For example a numeric field may only allow the digits 0–9, the decimal point and perhaps a minus sign or commas.

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It uses routines, often called "validation rules" "validation constraints" or "check routines", that check for correctness, meaningfulness, and security of data that are input to the system.For example the last digit of an ISBN for a book is a check digit calculated modulus 10.[3]Compares data in different systems to ensure it is consistent, e.g., The address for the customer with the same id is the same in both systems.The data may be represented differently in different systems and may need to be transformed to a common format to be compared, e.g., one system may store customer name in a single Name field as 'Doe, John Q', while another in three different fields: First_Name (John), Last_Name (Doe) and Middle_Name (Quality); to compare the two, the validation engine would have to transform data from the second system to match the data from the first, for example, using SQL: Last_Name || ', ' || First_Name || substr(Middle_Name, 1, 1) would convert the data from the second system to look like the data from the first 'Doe, John Q'Checks the data type of the input and give an error message if the input data does not match with the chosen data type, e.g., In an input box accepting numeric data, if the letter 'O' was typed instead of the number zero, an error message would appear.Rules can be collected through the requirements capture exercise.In evaluating the basics of data validation, generalizations can be made regarding the different types of validation, according to the scope, complexity, and purpose of the various validation operations to be carried out.For example, an experienced user may enter a well-formed string that matches the specification for a valid e-mail address, as defined in RFC 5322 but that well-formed string might not actually correspond to a resolvable domain connected to an active e-mail account.

Structured validation allows for the combination of any of various basic data type validation steps, along with more complex processing.

A validation process involves two distinct steps: (a) Validation Check and (b) Post-Check action.

The check step uses one or more computational rules (see section below) to determine if the data is valid.

For example if contact record in Payroll database is marked as "former employee", then this record must not have any associated salary payments after the date on which employee left organization (Cardinality = 0). An extra digit is added to a number which is calculated from the digits.

The computer checks this calculation when data are entered.

(See also data type checks below)Checks for missing records.