Tag: fallback accessor

Using artificial intelligence (AI) in testing to visually expand the accessor pool increases accuracy, productivity, and almost completely eliminates maintenance. The number one reason test cases get re-written is that an accessor has changed.  Using AI and image recognition provides more ways to recognize that accessor, which improves the stability and reliability of the test. It’s a transformational way for a test platform to recognize web elements that makes the traditional means of using accessors, or locators, arguably almost obsolete. What are accessors and how do they work? Accessors are the way that a test system can recognize an action,