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arXiv · 2301.08372

Screen Correspondence: Mapping Interchangeable Elements between UIs

Jason Wu·Amanda Swearngin·Xiaoyi Zhang·Jeffrey Nichols·Jeffrey P. Bigham
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Abstract

Understanding user interface (UI) functionality is a useful yet challenging task for both machines and people. In this paper, we investigate a machine learning approach for screen correspondence, which allows reasoning about UIs by mapping their elements onto previously encountered examples with known functionality and properties. We describe and implement a model that incorporates element semantics, appearance, and text to support correspondence computation without requiring any labeled examples. Through a comprehensive performance evaluation, we show that our approach improves upon baselines by incorporating multi-modal properties of UIs. Finally, we show three example applications where screen correspondence facilitates better UI understanding for humans and machines: (i) instructional overlay generation, (ii) semantic UI element search, and (iii) automated interface testing.

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Jason Wu, Amanda Swearngin, Xiaoyi Zhang, Jeffrey Nichols, Jeffrey P. Bigham. 2023-01-20. Screen Correspondence: Mapping Interchangeable Elements between UIs. https://arxiv.org/abs/2301.08372

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