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.