From UI to Code: Mobile Ads Detection via LLM-Unified Static-Dynamic Analysis
Shang Ma, Wei Cheng, Yanfang Ye, Xusheng Xiao

TL;DR
ADWISE is a novel framework combining static analysis and LLM-guided reasoning to improve mobile ads detection in complex UIs, outperforming existing methods.
Contribution
It introduces a unified approach that integrates static program analysis with LLM-based reasoning for effective mobile ads detection.
Findings
ADWISE outperforms baselines by 25.60% in ad widget detection.
It uncovers 34.34% more ad regulation violations.
The framework effectively combines static analysis with LLM reasoning.
Abstract
Mobile advertisements (ads) are essential to the app economy, yet detecting them is challenging because ad content is dynamically fetched from remote servers and rendered through diverse user interfaces (UIs), making ads difficult to locate and trigger at runtime. To address this challenge, we present ADWISE, a novel framework that formulates mobile ads detection as LLM-guided, ad-oriented UI exploration. ADWISE first performs static program analysis to identify UI widgets used to place ads, which we call ad widgets. It then uses a grounded LLM reasoning loop to navigate toward and trigger these widgets under three complementary domain guidance signals: (1) WTG-based guidance, which provides global transition priors from a statically constructed window transition graph (WTG); (2) semantic guidance, which reasons over app functionality to prioritize user-likely interaction paths; and (3)…
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