Style-Guided Web Application Exploration
This addresses the challenge of efficient web app testing and analysis for developers and testers, though it is incremental as it builds on existing exploration techniques with a novel method.
The paper tackles the problem of identifying actionable elements in web apps for automated exploration by proposing a browser-independent, instrumentation-free approach using structural and visual stylistic cues, achieving up to 90.14% precision and 87.76% recall for click events and improving JavaScript code coverage by up to 23%.
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\%.