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arXiv · 2308.08726

Never-ending Learning of User Interfaces

Jason Wu·Rebecca Krosnick·Eldon Schoop·Amanda Swearngin·Jeffrey P. Bigham·Jeffrey Nichols
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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 that are collected and labeled by human crowd-workers, a process that is costly and surprisingly error-prone for certain tasks. For example, it is possible to guess if a UI element is "tappable" from a screenshot (i.e., based on visual signifiers) or from potentially unreliable metadata (e.g., a view hierarchy), but one way to know for certain is to programmatically tap the UI element and observe the effects. We built the Never-ending UI Learner, an app crawler that automatically installs real apps from a mobile app store and crawls them to discover new and challenging training examples to learn from. The Never-ending UI Learner 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.

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Jason Wu, Rebecca Krosnick, Eldon Schoop, Amanda Swearngin, Jeffrey P. Bigham, Jeffrey Nichols. 2023-08-17. Never-ending Learning of User Interfaces. https://arxiv.org/abs/2308.08726

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