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Robust open-source frameworks and DevOps integrations have brought us a long way from the early days of QA testing with clunky tools. And just as we’ve transitioned from Waterfall to agile models, our testing needs to keep pace at every stage of development.
Automation helps, but identifying use case scenarios and keeping up with the coding requirements can be cumbersome. In this paper, you’ll learn innovative ways to solve the challenges of automation testing using artificial intelligence and machine learning.
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Innovators and Laggards: The Technology Landscape of 2019
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