Operational Test Data with DPLs

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Use

Appvance DPL TypesConfiguring Appvance DPLsProviding Data to manageDistributed operationalTestsDynamic DPLArray Generator DPLWhiteBoard DPLScenario Wide DPLHash DPLJavaScript DPLUsing the DPLsCommunication Intra-Steps Using TheLockerThe RSC ResourceCreating Your Own DPLSpecific Types of DPLsDPL Usage with Appvance Designer ScriptSynthetic DPLEncrypt/Decrypt DPLOperation Test Data FAQ

Appvance IQ's Data Production Library (DPL) is a powerful system to deliver data to tests at runtime. For instance, a test dataof foran Scenarioonline Editorregistration service stores name and Servicepassword Suitevalues assetsin whena testsdatabase needand repeatable,uses controlled,a orDPL generatedto values.access Treatthe DPLsdatabase as part of the test design,operates. In a second instance, a test of a reporting service uses a DPL to access a comma-separated-value (CSV) file of acceptable responses to validate service responses.

Software developers, QA technicians, and IT managers with a variety of technical skills use Appvance IQ. It is important to provide DPLs that are ready to use and require no programming skills. But also offer a framework for building your own DPLs. Appvance Unified Test

Appvance IQ (AIQ) Data Product Libraries (DPLs) delivers operational data to tests at test run-time. For instance, a recorded Appvance Designer Script or Selenium test of a Web-based log-in page retrieves the user name and password values from a comma-separated value (CSV) data file as AIQ executes the test. Tests can use DPL provided data not only as incidentalinput datavalues pastedbut intoalso individualto steps.validate the application's function.

Planning

The checklistAIQ

• DefineDPL whichsystem stepsprovides consumea eachsimple dataand value.
• Chooseeasy static,way CSV/file-backed,to synthetic,data-enabled dynamic,tests. A DPL does not require programming or service-backedscripting. data
  based on the test objective.
• Document reset, cleanup, ownership, and environment-specific differences.
• Avoid unsecured secrets, production customer data, and values that cannot be reproduced.

Validation workflow

1. Run a small data sample before scaling execution.
2. Confirm each row or generated value maps to the expected scenario behavior.
3. Review failures for data exhaustion, malformed values, duplicates, or environment mismatch.
4. Keep DPL changes under review when they affect regression results.

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Last updated: OctoberApril 6,11, 20262024

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