  Tapping is an immensely important gesture in mobile touchscreen interfaces,
yet people still frequently are required to learn which elements are tappable
through trial and error. Predicting human behavior for this everyday gesture
can help mobile app designers understand an important aspect of the usability
of their apps without having to run a user study. In this paper, we present an
approach for modeling tappability of mobile interfaces at scale. We conducted
large-scale data collection of interface tappability over a rich set of mobile
apps using crowdsourcing and computationally investigated a variety of
signifiers that people use to distinguish tappable versus not-tappable
elements. Based on the dataset, we developed and trained a deep neural network
that predicts how likely a user will perceive an interface element as tappable
versus not tappable. Using the trained tappability model, we developed TapShoe,
a tool that automatically diagnoses mismatches between the tappability of each
element as perceived by a human user---predicted by our model, and the intended
or actual tappable state of the element specified by the developer or designer.
Our model achieved reasonable accuracy: mean precision 90.2\% and recall
87.0\%, in matching human perception on identifying tappable UI elements. The
tappability model and TapShoe were well received by designers via an informal
evaluation with 7 professional interaction designers.
