Generative AI

Test Library Generation and Optimisation

Software testing based on early-stage AI-enabled system modelling provides a fundamentally different scale of system exploration, quality, test coverage and automation. The approach helps strengthen the operational resiliency of complex high-load high-availability systems. AI Testing can be applied to a wide variety of business domains and use cases due to its main analytical power being separated from the code. Compared to a traditional test library, developing or optimising a test library using AI-enabled model-based testing and automation introduces many business and operational benefits.


Test library development/optimisation based on industry-tested methods and extensive domain expertise.

Improved test library performance: system regression and diagnostics can be done fast and in an automated mode.

Wider test coverage, compared to traditional test libraries.

Testers can delegate test generation to AI and concentrate on higher-impact cognitively demanding tasks.

Limiting the test library to a controlled subset of tests helps reduce server load.

Improved ROI, due to the increased efficiency of the test library.

Our comprehensive AI-enabled testing approach relies on the principles of Model-based testing. We value exploring features and functionalities in their context of the system over merely satisfying sets of validation checks. We see clear value in taking the time to build a machine-readable twin of a system under test and analysing its behaviour under load. Our AI Testing approach helps us introduce efficiencies into existing testing processes or facilitate the development of new optimised test libraries. 

Using Generative AI helps us create massive amounts of test scenarios covering the system under test end-to-end. This volume is further reduced using a set of algorithms, with the final optimised subset providing the same level of test coverage. The level of test coverage can be further continuously improved using AI algorithms. Models can be reused to test and build systems with similar functionality.

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Q: Why does a test library need optimisation?
Why can’t we use the entire volume of variables for model training?


Q: Can the approach be used on new or existing test libraries?