Style-Guided Web Application Exploration

TL;DR

This paper introduces StyleX, a machine learning-based tool that predicts and ranks actionable web elements using stylistic cues, significantly enhancing web app exploration efficiency without relying on instrumentation.

Contribution

The paper presents a novel, browser-independent approach leveraging stylistic cues and ML models to identify actionable web elements, improving exploration coverage over existing methods.

Findings

Achieves 90.14% precision and 87.76% recall in predicting click actions.

Improves JavaScript code coverage by up to 23%.

Uses training data from 700,000 web elements across 1,000 websites.

Abstract

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\%.