AI for element selection - Jason Arbon | SeleniumConf Chicago

A core pain point in the development and maintenance of Selenium and Appium test scripts is the fragility of element selection. Elements are traditionally identified in the application for interaction or validation using XPATH, CSS, Accessibility IDs, or other element attributes. There are two primary issues with these approaches. First, it is slow and cumbersome to identify/create the XPATH, CSS or other selectors. Second, as the application changes from build to build, these selectors often break and need to be manually updated.

This presentation describes a method of directly applying Artificial Intelligence (AI) and Machine Learning (ML) techniques to quickly build more robust element selectors. A discussion of the underlying open technology, processes, measurements of correctness and robustness will be presented. Get a glimpse of the future of element selection, and learn enough technical detail to apply it in your own test automation.

About Jason Arbon
Jason Arbon is the CEO of test.ai where his mission is to apply AI and machine learning techniques to automate the world’s apps, and universally improve their quality. He was formerly the director of engineering and product at Applause.com/uTest.com, where he led product strategy to deliver crowdsourced testing via more than 250,000 community members and created the app store data analytics service. Jason previously held engineering leadership roles at Google (Chrome, Search) and Microsoft (Bing, SQL Server, WindowsCE). Jason also co-authored How Google Tests Software and App Quality: Secrets for Agile App Teams.

Видео AI for element selection - Jason Arbon | SeleniumConf Chicago канала Selenium Conference