machine learning and pattern recognition
Additionally, the new Performance Engineering service uses this unique and powerful combination of Machine Learning and Pattern Recognition to manage and model solution capacity.
The algorithm behind the service is the result of years’ of research and practice and provides the ability to accurately forecast capacity requirements in a wide range of environments and settings. This can yield significant resource savings in larger deployments, including test functions, and improve efficiency across the board.
Enquire Now
actionable insights
Many enterprises have historically encountered the obvious problem of under-provisioning hardware and cloud resources with the simple but inefficient tactic of over-provisioning. With Edge’s new Performance Engineering service this is no longer a risk that clients need to manage.
By creating actionable insights from existing provisioning and capacity data, the new service can predictably and automatically manage test capacity too much tighter tolerances, removing the guesswork from test capacity provisioning. The technique can be applied to all aspects of test capacity, from predicting cloud service requirements and scheduling them at the most cost-effective time, through to accurately specifying on-premise hardware requirements.
Enquire Now
performance testing combination
Performance Engineering offers a deeper view of the testing environment, delivering an early understanding of performance, making it a powerful complement to more traditional performance testing. Performance Engineering can enable earlier and more cost-effective interventions than traditional testing processes, although it should not be viewed as a replacement.
Full solution, end-to-end load testing still brings value in terms of understanding the likely user experience. In combination with performance engineering, the traditional performance test becomes more of a one-off acceptance test rather than an expensive late phase iterative activity. The resultant benefit of this combined approach being the reduction of the overall cost of quality.
Enquire Now