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System Development Lifecycle

System Development Lifecycle. Verification and Validation. Learning Objectives. Describe the need for the accurate input of data and the ways in which we can check that the data is correct. Analysis. Obsolescence. Information Collection. Identification. Maintenance. Implementation.

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System Development Lifecycle

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  1. System Development Lifecycle Verification and Validation

  2. Learning Objectives • Describe the need for the accurate input of data and the ways in which we can check that the data is correct.

  3. Analysis Obsolescence Information Collection Identification Maintenance Implementation Feasibility study Development and Testing Design

  4. Design Phase Continued

  5. Data Validation Routines • Check input data is sensible before accepting and then processing it.

  6. Presence Check • This checks that important data is actually there and has not been missed out. • E.g. Customers may be required to give their telephone numbers or surname. • Can only be used to check something has been input not check if the input is sensible. • For example, if asked to validate a name, a presence check cannot be used as “something” has already been entered. You can only use a presence check on a input to check if an input has been made.

  7. Existency • A code exists. • E.g. • Bar code exists in the stock file. • Dates actually exist e.g. 31/04/07 or 29/02/07 do not exist but are in the correct format.

  8. Consistency check • Checks fields to ensure data in these fields corresponds. • e.g. If Title = "Mr.", then Gender = "M".

  9. Range Check • This checks that the data lies within a specified range of values. • e.g. • The month of a person's DOB should lie between 1 and 12. • A telephone bill amount is less than some maximum value for consumer bills.

  10. Type • A check that data is of the right type.   • E.g. • Number, Text etc…

  11. Format Check • A check that data is in a specified format.   • E.g. • Dates have to be in the format DD/MM/YYYY. • Most ID numbers and codes have requirements which can undergo format checking. • Only for regular expressions where there is a strict format requirement where the number of characters and/or digits for each part of the format is also restricted.

  12. Character check • Check for / Reject particular characters. • E.g. • No numbers in text. • Hyphen, apostrophe or @ followed by a . (email addresses).

  13. Length Check • Checks that fields have the correct number of characters / digits. • E.g. • A bank account number may always be 10 digits long. • A date must have at least 6 numbers in it. • A phone number has 12 numbers.

  14. Check digits • Used for numerical data. • An extra digit is added to a number which is calculated from the number using modular division (dividing by a number and using the remainder as the extra digit) . • The computer divides by the same number to check this digit. • e.g. modulo-11 multiplies each digit by its digit position, adds the totals together and divides the result by eleven. • An ISBN number on a book is 420351_ where _ is the check digit. • The check digit is digit position 1 (as it is the 1st place value). • Sum = (1*2)+(5*3)+(3*4)+(0*5)+(2*6)+(4*7) • = 2+15+12+0+12+28 = 69 • 11*6 = 66 • So 69/11 = 6 remainder 69-66= 3 which is the check digit. • So the full number entered is 4203513

  15. Batch Totals • This checks for missing records. • Numerical fields may be added together for all records in a batch. • The batch total is entered and the computer checks that the total is correct. • E.g. Add the 'Total Cost' field of a number of transactions together.

  16. Hash Totals • This is just a batch total done on a meaningless field. • E.g. Add the Telephone Numbers together for a number of Customers.

  17. Other Validation Checks http://en.wikipedia.org/wiki/Data_validation

  18. Verification • Used to prevent errors occurring when data is copied from one medium to another. • e.g. paper to disk, disk to disk, memory to disk

  19. Double entry • Typing the data in twice and comparing the two copies. • Time consuming • Costly

  20. Proofreading data • Checking what is on the screen is the same as on the input document. • Time consuming • Costly

  21. Plenary • What is validation? • Check input data is sensible before accepting and then processing it. • What is verification? • Used to prevent errors occurring when data is copied from one medium to another.

  22. Plenary • List all validation methods. • List both verification methods.

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