Applications and databases have their own logic and unique properties. For test data to be realistic and helpful, it must reflect production characteristics such as:
• selection conditions (business rules)
• data attributes and transformations
• inter-field/key relationships (referential integrity)
• value ranges and calculations
To ensure test tables and files have the right spectrum of data so that applications are pre-stressed to production conditions, test data must accurately reflect:
• type - field values in the correct form
• width - values in current (and future) range
• depth - volumes address scalability concerns
Solutions:
Applications developed with realistic data formats and volumes are more likely
to succeed against future production data. IRI's test data creation package, RowGen, builds custom test files containing randomly-generated data,
or containing data that is randomly selected from real set files.
To produce the right volumes and value ranges for your data, RowGen features conditional selection and formatting capabilities that create test data in specified distributions and parameters. It can also enhance the realism of the test data by (simultaneously) transforming and formatting the output targets. For example, you can randomly select data - and specify ranges - from pools of real data and weighted numbers to populate output(s) that look and behave right in your application environment.
By using any combination of set-file definition and selection, field-level conditions and manipulations, as well as custom transformation functions, RowGen builds the business intelligence you need into your test data so your applications can be thoroughly stressed and vetted, improving the quality of your deliverables and the reliability of your processes.
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