Optical character recognition based-on system for automated software testing

The paper presents the development and deployment of an artificial intelligence (AI) test automation framework that allows testers to more fluidly develop scripts and carry out their day-to-day tasks. In particular, the framework aims to speed up the test automation process by enabling its users to locate elements on a webpage through the use of template-matching-based image recognition as well as optical character recognition (OCR). Indeed, test automation specialists spend much of their time creating page-object models (POMs), where they capture elements on the screen via complex locators such as cascading style sheet (CSS) or XPath. However, when webpages are updated or elements are moved around, locators become void, eventually pointing to nothing unless written in such a dynamic way as to prevent this. This heavily relies on developers providing meaningful tags to elements that they can then be located by, whereas with the introduction of an image recognition engine in our AI fr amework, this tedious and long-winded approach has been be shortened.