The power of aI testing
AI driven autonomous test creation and execution is a breakthrough in software testing. Our service eliminates labor, increases application coverage to near 100% of code, page and actions, finding more bugs faster than manual testing or even traditional scripting.
With minor human-assisted training, QAaaS generates a comprehensive set of user flows for any application: browser-based, mobile web or native mobile. We leverage Machine Learning algorithms to create thousands of scripts to ensure bugs are caught before release to production. Best of all, QAaaS’s AI informed, machine generated test portfolios are vastly more comprehensive – massively reducing your risk of introducing bugs into production.
Immediate Testing Results
Our QAaaS AI driven autonomous testing generates and executes comprehensive end-to-end tests autonomously, with little input at the speed of CI/CD. No recording, scripting or logs are required. Rich test execution dashboards and reports are then available for review.
QAaaS’s army of smart bots learn and map the Application-Under-Test by exploring each and every path through it – not just the usual “happy path”. They create a comprehensive set of use cases as they go. These use cases number from the hundreds to the thousands, and result in near 100% coverage of the AUT.
QAaaS’s proprietary UI Intelligence Library understands how to navigate controls from a wide variety of frameworks, for both browser and mobile apps. This includes apps using HTML5, React, Angular and many others. UI Intelligence, coupled with QAaaS’s AI Hinting, allows its army of smart bots to navigate through every possible user path.
Our QAaaS service utilizes machine learning bots to conduct a comprehensive examination of the Application Under Test (AUT). These bots work in tandem to explore every possible path of the AUT, uncovering use cases that may have been overlooked by testing teams and other stakeholders. As they traverse through the application, they generate a detailed blueprint of its pages and flows. Upon completing a path, the bots begin anew, charting different routes until every use case is identified and mapped, and all possible test cases are generated. The results we obtain are derived directly from these AI-powered bots, which can assimilate insights from production logs showcasing real user actions.