Data Driven Test Automation for Micro Focus UFT: A Model-Based Approach
Book a demo to get started today: https://hubs.ly/Q02vjL3S0
Visit the Curiosity Website: https://hubs.li/Q01hmm6n0
Learn more about Quality Modeller: https://hubs.li/Q01XLxV50
Learn more about Enterprise Test Data: https://hubs.li/Q01XLxS_0
Follow Curiosity’s socials
LinkedIn: https://hubs.li/Q01XLxWv0
Twitter: https://hubs.li/Q01XLxT70
Facebook: https://hubs.li/Q01XLxWb0 Automatically generate UFT data sheets that contain every distinct combination of data, for rigorous and robust data driven testing. Test Modeller finds, makes and prepares data “just in time” for Micro Focus UFT, providing accurate data for every test on demand.
Test Modeller eliminates bottlenecks created by test data, automatically preparing a set of data that is complete, executable, and available on demand. Complete test data is prepared “just in time” during test executing, ensuring that every UFT test comes equipped with valid and up-to-date data. This leverages a re-usable catalogue of test data processes, empowering testers to find, make, subset and clone data as a standard step in test automation. With Test Modeller, QA teams can rapidly create data sets with which to drive rigorous UFT testing, working rapidly and in parallel from on demand data.
Watch this short demonstration of rigorous UI testing against a CRM system to see how:
1. Test Modeller automatically registers parameterised Micro Focus UFT scripts and corresponding data tables, enabling automated generation for existing frameworks.
2. Automated test generation algorithms generate a set of test data that "covers” every data combination needed for rapid and rigorous UFT testing.
3. A test data catalogue embeds re-usable TDM processes at the model level, finding and making up-to-date test data automatically as tests are generated.
4. The test data catalogue means that a TDM process only needs to be configured once, enabling testers to parameterize and re-use it in parallel from a simple form.
5. Automated data look-ups hunt for data for tests as they are generated, going directly into databases, or via APIs and front-end applications.
6. A full range of test data utilities prepares data automatically as a standard step in automated test execution, finding, subsetting and cloning data in batch.
7. Synthetic test data generation automatically creates any new data required, rapidly producing a set of data driven tests that hit every positive and negative combination.
8. “Just in time” test data preparation ensures that data is up-to-date and valid for each test, avoiding the delays created by automated test failures.
9. Assigning unique values to each and every test avoids the bottlenecks created when one test consumes data needed by another test.
10. Test data is validated and updated each time tests are generated and run, making sure that each test has valid and up-to-date data associated with it.
11. Multiple coverage profiles target testing on high-risk or critical data combinations, reducing the number of UFT tests without undermining testing quality.