AI and Machine Learning in a Selenium World

Many testing teams today use Selenium for their automation needs, and rightfully so: Selenium allows for cross-browser and mobile support, it’s free, and it has a large open source community behind it. Regardless of its feature set, Selenium—and test automation in general—has its own list of pain points that can have a large impact on testing metrics, depending on the test architecture used and the design patterns being followed. To address these pain points, a team can use artificial intelligence (AI) and machine learning (ML) via open source libraries. This enables the tests to find their own elements and attempt every possible combination of test cases, which a tester could then store, rerun, and iterate on. This approach effectively gives Selenium a brain in the form of models and actions. Join Mike Wagner to gain an understanding of convolutional neural networks and how they apply to element selection, reinforcement learning and how it applies to test coverage, and an approach for implementing convolutional neural networks into an existing Selenium framework. Learn how to integrate AI into your existing Selenium framework.