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AI/ML have become significant areas of research and development for every industry, and QA is no different. The practice of testing applications has to a large degree become dominated by the principles and patterns of software development. As with any set of tools susceptible to automation, there is the potential for even greater advances due to the application of AI and ML. But in the case of the QA industry, does the marketing behind these advances match the math?
In this session, test automation expert Jonathan Lipps examines some of the claims about AI/ML achievements in the QA industry in light of a brief introduction to AI and ML themselves, to see whether the claims are actually saying or doing anything useful.
https://www.youtube.com/watch?v=4JRTYYpXBs0
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HeadSpin helps Telcos, media organizations, and large enterprises analyze and improve the user experience of their digital products through its global real device infrastructure, on the edge end-to-end testing, and ML-driven performance and quality of experience analytics.
The HeadSpin data science platform enables collaboration among global teams to accelerate release cycles, build for complex real user environments, and proactively detect and resolve issues whether at the code, device, or network layer. HeadSpin currently works alongside a number of global telco and media organizations today to:
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