It employs a model-based, no-code approach for end-to-end intelligent test automation, bridging existing and new solutions, enabling banks to run and transform their business at the same time, by modernising their core systems and achieve better cost optimisation and business resiliency while approaching a "digital to the core" strategy.

It helps banks test, monitor, and analyse their end-to-end customer experience 20x faster than open source or traditional tools bringing down the costs of QA by 90%

AI, ML and RPA at the core

Combining the best of automation approaches with RPA, AI and ML, Validata Sense.ai delivers superior, resilient automation focusing mainly on three aspects – to eliminate test coverage overlaps, optimize efforts with more predictable testing and move from defect detection to defect prevention.

It prioritises and identifies the optimal user journeys and test paths to maximise test coverage with less time, cost and effort.

• AI can learn and copy humans and in doing so, eventually it will allow the algorithms to do the testing. Unlike algorithms that use supervised learning which requires laborious amounts of labeled data, unsupervised learning on unlabeled data can be used which means less human effort.
• AI explores paths that a human would never think of and intelligently refines exploration to maximise coverage
• AI predicts how the release will impact customer satisfaction

Smart Test Data Management

Test data management and Environments provisioning is a key part of the CI/CD process. You cannot achieve true CI/CD flow without addressing data and environments provisioning, which is getting the right data to the right environment, making it accessible by the right people at the right time in a secure and compliant manner.

Validata Sense.ai offers a complete, model-based test data management solution driven by requirements and powered with AI and machine learning. Aligning test data to requirements ensures that it is ‘fit for purpose’, while the ability to provision it quickly to test teams means that they can quickly respond to the changing demands of the business.

Users can instantly synthesise and create complex data sets based on business rules and constraints and with different combinations and permutations. Leveraging AI, it is able to intelligently recommend data sets that best fit your test scenarios and suggests next-best-actions, addressing data privacy and compliance issues with data masking at the same time.

Autonomous Testing

Autonomous, in terms of automated testing, means the ability to create test cases, execute them without human intervention and be capable of self-healing identifying the issues and the corrective actions. Testers can trigger unattended test cycles, where defects are identified and remedial actions are triggered in run time, based on insights collected from historical data and past events.

Autonomous capabilities include:

• Zero code, zero touch, automated test case generation
• Autonomous test management
• Autonomous test data management: synthesise, mask/anonymise, virtualize, subsetting & self-service provisioning of test data
• Self-correcting tests through continuous deep-learning
• Autonomous analytics, AI- generated recommendations and ‘next best action’