Never-ending Learning of User Interfaces

Abstract

Machine learning models have been trained to predict semantic information about user interfaces (UIs) to make apps more accessible, easier to test, and to automate. Currently, most models rely on datasets of static screenshots that are labeled by human annotators, a process that is costly and surprisingly error-prone for certain tasks. For example, workers labeling whether a UI element is "tappable"from a screenshot must guess using visual signifiers, and do not have the benefit of tapping on the UI element in the running app and observing the effects. In this paper, we present the Never-ending UI Learner, an app crawler that automatically installs real apps from a mobile app store and crawls them to infer semantic properties of UIs by interacting with UI elements, discovering new and challenging training examples to learn from, and continually updating machine learning models designed to predict these semantics. The Never-ending UI Learner so far has crawled for more than 5,000 device-hours, performing over half a million actions on 6,000 apps to train three computer vision models for i) tappability prediction, ii) draggability prediction, and iii) screen similarity.