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Test Modeller for GitLab

Optimise the coverage of test automation code stored in GitLab with Test ModellerFacilitate enterprise-wide test automation adoption by minimising reliance on scripting, avoiding test data bottlenecks, and reducing manual test maintenance. 

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Maximise test automation ROI

Test Modeller introduces all the benefits of Model-Based techniques to test automation frameworks stored in GitLab. You can eradicate unnecessary manual scripting with automated test generation, while ensuring that all the data needed for continuous test automation is available on demand. Re-usability of existing test code means that a small core of skilled engineers can feed in new code, enabling QA teams enterprise wide to automate their own tests. Automated test maintenance time is also cut drastically, facilitating rigorous automated testing that can keep up with fast-changing, complex systems.


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Enterprise-wide test automation adoption

Watch this short example of Test Modeller working with a Selenium-Java framework to see how:

  1. Test Modeller automatically parses Java, C# or Python code, importing its core objects and actions in minutes.

  2. The automation logic becomes fully re-usable, overlaying it onto flowchart models that are quick-to-build and easy to maintain. Connectors are provided to import test cases and requirements, with full BDD and TDD support.

  3. Anyone can build automated tests for complex systems. An intuitive “low code” test builder leverages code from existing frameworks, combining the flexibility of coded techniques with the simplicity of low code approaches.

  4. Automated tests detect more defects first time round, using automated algorithms to generate the smallest set of tests needed to “cover” the model.

  5. Test data for every test is defined at the model level, using over 500 dynamic functions that resolve during test creation.

  6. Automated test scripts and data can be generated for commercial, open source and homegrown frameworks, avoiding vendor lock and testing across the whole testing pyramid from one tool. 

  7. The optimised tests and data can be synchronised back to existing GitLab projects, using CI/CD pipelines to execute them.

  8. Run results are synchronised with the models in Test Modeller, as well as across Application Lifecycle Management (ALM) and Project Management tools. 

  9. Granular analysis of test failures reduces defect remediation time, while test automation frameworks are integrated into DevOps pipelines.

  10. The automated tests and data can be updated in minutes as the application changes, simply updating the central models and re-generating tests. 

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