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Validata Sense.ai receives Innovation Award in China

Added: (Fri Jun 28 2019)

Pressbox (Press Release) - London, UK , 27th June 2019 – Validata Sense.ai, the award- winning platform at the Shanghai Pudong Development Bank (SPDB) 2nd International Fintech Innovation Competition in China, delivers coverage and defect analytics that allow QA and DevOps teams to achieve maximun test coverage, lower development costs and a reduced project impact.

The SPDB Innovation Jam featured twelve other contesting companies and the winners were selected by both a judges’ committee and audience members.

The coverage and defect analytics functionality within Validata Sense.ai, is based on the use of advanced machine learning algorithms and AI techniques. It is able to ingest production, defects and event data, frequent transactions and system load from digital banking systems to provide deep insights, identifying the optimal workflows and testing paths, and the ‘next best actions.’

In doing so, it allows DevOps teams to understand why there are problems with an upgrade or a release, rather than simply receive failure notifications, focusing testing on the parts of the user experience that matter, and delivering real value by providing answers with more relevance and context.

Vaios Vaitsis, Founder and CEO at Validata Group commented, “We are proud that Validata Sense.ai has been recognized for the value it brings to Temenos digital transformation projects. It embraces continuous intelligent automation, AI and cognitive robotic process automation, enabling organisations to test the end-to-end customer experience and predict the user impact of a new product version prior to release.

The deployment of the platform is expected to enable banks to reduce development costs by 25% and accelerate Temenos implementation and upgrade projects, ensuring amazing customer experience and achieving faster time to value, linking testing to business outcomes.”

By connecting to the clients’ production and defect data, it looks for common failure patterns, analyses them to refine tests, and drives to the precise root cause of the issues enabling the assignment to the correct team, reducing defect turnaround time and improving productivity.

Through the AI-generated recommendations and predictions, it enables project stakeholders to manage their projects better and faster by getting contextual insights of the defect metrics. By forecasting each week’s bugs, it brings efficiencies in planning and resourcing, turning around bug fixing time in minutes rather than weeks and days.

Through its revolutionary model-driven approach powered by AI, it is able to automate more and provide better testing in less time, empowering teams to get a better result than manual testing and provide better user experience.

The tool’s test case generation engine including test data, eliminates the data wait with on demand data access and assists the user to perform automated tests as and when required, with optimal coverage against business risk. Teams can now deliver a 24 days’ worth of testing in 24 hours, and save up to 60% of the analysis time spent on test case design and bug fixing.

Submitted by:Sofia Xylia
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