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. Glossary

Data Generation Overview

Data Generation Overview

Use data generation to create realistic test data for test environments. You can define generation rules that define the logic to generate data.
Import metadata into a project to define the type of data that you want to generate. You assign generation rules that you create or default rules to the target columns to generate data based on the data type of the column. When you create a rule, you can choose a generation technique and configure parameters to create random test data.
If a table name or a column name contains special characters, the data generation workflow fails.
To implement data generation, you create a data generation plan and a workflow from the plan. If the target is a flat file, you can configure test tool integration properties in the plan. Configure test tool integration to copy the results to a location in an integrated HP ALM server. To store the test data along with the metadata in the test data warehouse, select the test data warehouse as the target connection.

Data Generation Example

You work for an organization that sells plane tickets. You want to generate data in tables that contain customer information such as identification number, status of membership, and address. You want additional tables to store ticket details such as ticket number and flight number. To generate the data, you can perform the following tasks:
  • Create data generation rules that load dictionary values such as names into the tables.
  • Create random number strings for ticket numbers.
  • Create a numeric sequence for identification numbers.
  • Use a reference lookup for values such as airport codes.
  • Create projects to import metadata, enable relationships, and create entities.
  • Make rule assignments, create a plan, and run the plan to generate the data.