Table of Contents


  1. Preface
  2. Introduction to Test Data Management
  3. Test Data Manager
  4. Projects
  5. Policies
  6. Data Discovery
  7. Creating a Data Subset
  8. Performing a Data Masking Operation
  9. Data Masking Techniques and Parameters
  10. Data Generation
  11. Data Generation Techniques and Parameters
  12. Working with Test Data Warehouse
  13. Analyzing Test Data with Data Coverage
  14. Plans and Workflows
  15. Monitor
  16. Reports
  17. ilmcmd
  18. tdwcmd
  19. tdwquery
  20. Data Type Reference
  21. Data Type Reference for Test Data Warehouse
  22. Data Type Reference for Hadoop
  23. Glossary

Random Generation

Random Generation

A random generation rule is a global or ad hoc rule that creates random string, numeric, and date values. You can use random generation to generate data such as salary and birth dates.
When you configure random generation, you can specify a minimum and maximum length for data strings and a minimum and maximum value for numeric data. You can include a regular expression to create a fixed percentage of the generated data. The percentages of the pattern distributions must total 100. When you generate dates, you can select a start date and end date to create random dates within a range.
Random generation rules can be global rules or ad hoc rules.
The following image shows the random generation parameters:
The random generation parameters are minimum length, maximum length, pattern, distribution percent, generate unique values, null values, and invalid values.

Random Generation Example

You want to generate addresses for customers. You create a rule that uses the random technique and the string data type. You define a regular expression in the rule. The regular expression creates rows with a four digit number, a random string of 12 characters, and "St." or "Ave." You set the pattern percentage to 100 to generate all the rows with the regular expression.