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.
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.
Why can’t we use the entire volume of variables for model training?
Most often, test environments are not that strong and, in some markets, such as those involving DLT or other slow technology, we have to be even pickier in selecting possible actions.
New libraries, on the other hand, can be created from scratch using our data-driven AI Testing approach with the optimisation logic embedded into the test library generation algorithm.