Automated test generation for data-driven frameworks

Create optimised data on-the-fly for continuous data-driven testing. Test Modeller avoids the time spent maintaining complex data in spreadsheets for test automation, optimising testing based on risk to find more bugs while they are quicker and more affordable to fix.

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Data-driven testing or data-driven bottlenecks?

Data-driven test automation is only as rigorous as the test data that feeds it, and only as fast as the time spent maintaining that data. Testers today often feed in data via spreadsheets, maintaining a complex set of files for different test scenarios. The data might be pulled from production, creating compliance risks in less-secure non-production environments, or it might be made by hand. Either way, the data combinations available typically fall far short of the logical variations contained in today’s systems, which far exceeds the “happy path” values commonly found in production, as well as the time available to make data in-sprint. Overall, testing with a narrow slice of Excel data risks costly bugs, non-compliance, and testing bottlenecks, calling for a new approach to data-driven test creation.

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Continuous, data-driven testing

With Test Modeller, rigorous data-driven testing occurs seamlessly as part of CI/CD, dynamically pulling data and generating optimised scripts for fast-changing systems. Test Modeller imports data stored in spreadsheets and beyond, quickly building a structured and easy-to-use repository of data. Automated model generation converts data sheets in the repository into dynamic data models, creating intuitive flowcharts of the system-under-test. The easy-to-maintain flows provide collaborative documentation for complex systems, while enabling risk-based test generation for data-driven test automation frameworks.

data-driven test automation using Test Modeller

Test Modeller provides a range of test generation algorithms that optimise data-driven testing based on time and risk. The click of a button or automated CI/CD process generates the smallest set of test cases and scripts needed to satisfy a given risk profile, pulling rich rows of data on-the-fly for continuous test execution. As critical systems change, testers are no longer forced to hack complex data or manage cumbersome folders of spreadsheets. They can quickly update central models to maintain the data-driven tests needed to find bugs earlier and at less cost to fix.

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Automated test generation for data-driven frameworks

Watch this demonstration of rigorous data-driven test automation using a Selenium Java framework to test an eCommerce system, to discover how:

  1. Test Modeller automatically converts test data spreadsheets into dynamic data models, creating living documentation of the system under test and enabling optimised test generation.

  2. Applying automated test case generation algorithms to the intuitive visual flowcharts creates the smallest set of data-driven tests needed to satisfy a defined risk profile.

  3. The automated test generation pushes optimised rows of data to existing data-driven test automation frameworks, finding bugs earlier and at less cost to fix.

  4. Automated test script generation pulls the parametrised data from the dynamic data models, generating executable test scripts that come with complete data embedded.

  5. Executing the optimised tests within CI/CD infrastructure enables rigorous continuous data, pulling test data on-the-fly to remove test creation and maintenance bottlenecks.

  6. Maintaining data sheets in a central repository and dynamic models quickly and consistently keeps tests up-to-date, testing fast-changing systems rigorosuly in-sprint.

  7. Visual data models make it quick and easy to spot missing data combinations and test logic, quickly augmenting data-driven tests to boost coverage and de-risk rapid releases. 

 

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Discover how Curiosity can help you accelerate and optimise your data-driven testing.

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