  A wide range of analysis and testing techniques targeting modern web apps
rely on the automated exploration of their state space by firing events that
mimic user interactions. However, finding out which elements are actionable in
web apps is not a trivial task. To improve the efficacy of exploring the event
space of web apps, we propose a browser-independent, instrumentation-free
approach based on structural and visual stylistic cues. Our approach,
implemented in a tool called StyleX, employs machine learning models, trained
on 700,000 web elements from 1,000 real-world websites, to predict actionable
elements on a webpage a priori. In addition, our approach uses stylistic cues
for ranking these actionable elements while exploring the app. Our actionable
predictor models achieve 90.14\% precision and 87.76\% recall when considering
the click event listener, and on average, 75.42\% precision and 77.76\% recall
when considering the five most-frequent event types. Our evaluations show that
StyleX can improve the JavaScript code coverage achieved by a general-purpose
crawler by up to 23\%.
