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Computer Science > Human-Computer Interaction

arXiv:2308.08726 (cs)
[Submitted on 17 Aug 2023]

Title:Never-ending Learning of User Interfaces

Authors:Jason Wu, Rebecca Krosnick, Eldon Schoop, Amanda Swearngin,
Jeffrey P. Bigham, Jeffrey Nichols
Download a PDF of the paper titled Never-ending Learning of User
Interfaces, by Jason Wu and 5 other authors
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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.

Subjects: Human-Computer Interaction (cs.HC)
Cite as:  arXiv:2308.08726 [cs.HC]
          (or arXiv:2308.08726v1 [cs.HC] for this version)
          https://doi.org/10.48550/arXiv.2308.08726
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Jason Wu [view email]
[v1] Thu, 17 Aug 2023 01:54:40 UTC (2,810 KB)
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