Panaya and Tricentis develop first autonomous SAP test platform


Jake Klein, Panaya

In an industry first, a platform enabling fully autonomous software testing in SAP environments will eliminate the need for time-consuming manually-engineered test scripts as well as their inherent risks.

The “zero-touch” platform has been introduced by software test automation supplier Tricentis and SaaS-based test management provider Panaya.

The platform harnesses machine learning to observe and understand patterns in end user transactions and automatically convert usage patterns into automated and repeatable business process tests. The platform eliminates redundant regression testing and reports changes in the SAP environment that could impact critical business processes.

The platform combines the test automation capabilities of Tricentis Tosca with the cloud-based end-to-end test management capabilities of Panaya Test Centre. The result is autonomous, continuous testing that keeps pace with changes in business processes.

“Tricentis and Panaya co-developed this breakthrough in testing to relieve SAP key users from the burden of manually creating, executing, and maintaining business process tests,” said Wolfgang Platz, founder and CPO, Tricentis.

Jake Klein, CEO, Panaya said that the new platform “addresses all aspects of a modern testing suite including automated process discovery, automatic test execution, manual test acceleration and test management”.

Autonomous SAP testing is expected to solve issues experienced in both automated and manual testing, enabling new or changed SAP business processes to be introduced more quickly and with less business risk.

Platz cited a recent survey of the SAP installed base that found about 72 per cent of SAP installations are currently verified using manual tests.

The platform will increase the speed and accuracy of such installations, eliminating manually engineered scripts and enabling zero-touch test case creation and maintenance. Quality teams will be able to protect critical business processes by observing actual usage patterns from production, reducing the risk of imperfect business knowledge transfer.

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