Test Data Explosion via the UI: Automatically Enrich the Coverage of Existing Data Sets

Automatically explode the coverage of existing data, creating new combinations to drive more rigorous testing. Test Modeller combines the dimensions found in existing data sets, rapidly providing a comprehensive spread of values to drive more rigorous test execution. 

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Production Data Cannot Test Rigorously

Existing data sets are usually incapable of testing the latest system release rigorously, particularly if testing still rely on copies of production data. These unwieldy data sets are slow to mask and provision, and yet contain just a fraction of the data combinations needed for sufficient test coverage.

The production data is drawn from past user behaviour and lacks the combinations needed to test unreleased functionality. Users furthermore typically behave as the system intends, and repetitious production data therefore lacks the outliers and negative values needed for testing rigour. Testing with masked and subsetted copies alone in turn leaves systems exposed to costly and time-consuming bugs, but manually creating additional data is slow and error prone. Rigorous testing instead requires an automated approach. 

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Maximise Data Coverage In-Sprint

Test Modeller empowers testers to enhance the coverage of existing data. Parallel test teams can shift automated testing left, rigorously testing new and existing functionality before the next release.  

A re-usable automated process combines the data dimensions found in existing test data. The automated data creation can be performed using existing automation frameworks and harnesses, mapping data dimensions to easy-to-use flowcharts. QA teams only need to specify a simple bit of SQL to find the relevant data variablesapplying automated coverage algorithms to generate an optimised set of values. The enhanced data combinations are then fed through the UI, direct into databases, or via flat files and APIs. This quickly creates a complete and varied database for testing and development, with unique values to avoid data clashes during data-driven test automation. 

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Rapidly Enrich Existing Data Sets

Watch this short example of using Micro Focus UFT to feed exploded data through a web UI, and discover how:

  1. Coverage-optimized data is generated rapidly from quick-to-build models of existing data dimensions.

  2. Exploding the coverage of back-end databases provides a simple technique for enhancing the quality of data rapidly.

  3. Re-usable test data processes make data explosion as simple as dragging-and-dropping blocks to a visual model, and providing a short piece of SQL to find the relevant dimensions.

  4. Multiple algorithms exploded the existing data values to the requisite level, generating a data set for more rigorous and robust testing.

  5. The rich data combinations are fed can be fed directly into databases, or via flat files, APIs and the front-end, quickly producing a completed data set.

  6. Matching test cases to unique data records avoids data clashes, ensuring stable test execution without automated test failures.

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Discover how you can accelerate your UI testing by speaking with a Curiosity expert.

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