Li Tao

Tao Li, Zilin Bian, Haozhe Lei et al.

Traditional mobility management strategies emphasize macro-level mobility oversight from traffic-sensing infrastructures, often overlooking safety risks that directly affect road users. To address this, we propose a Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics (DT-DIMA) system. The DT-DIMA system integrates real-time traffic information from pan-tilt-cameras (PTCs), synchronizes this data into a digital twin to accurately replicate the physical world, and predicts network-wide mobility and safety risks in real time. The system's innovation lies in its integration of spatial-temporal modeling, simulation, and online control modules. Tested and evaluated under normal traffic conditions and incidental situations (e.g., unexpected accidents, pre-planned work zones) in a simulated testbed in Brooklyn, New York, DT-DIMA demonstrated mean absolute percentage errors (MAPEs) ranging from 8.40% to 15.11% in estimating network-level traffic volume and MAPEs from 0.85% to 12.97% in network-level safety risk prediction. In addition, the highly accurate safety risk prediction enables PTCs to preemptively monitor road segments with high driving risks before incidents take place. Such proactive PTC surveillance creates around a 5-minute lead time in capturing traffic incidents. The DT-DIMA system enables transportation managers to understand mobility not only in terms of traffic patterns but also driver-experienced safety risks, allowing for proactive resource allocation in response to various traffic situations. To the authors' best knowledge, DT-DIMA is the first urban mobility management system that considers both mobility and safety risks based on digital twin architecture.

Chenhui Cui, Tao Li, Junjie Wang et al.

Mobile apps have become essential, making quality assurance increasingly important. GUI testing is widely used for automated exploration, yet text-input components remain a major obstacle, as many UI pages require semantically appropriate text inputs before proceeding. Large Language Models have shown promise in generating context-aware text, but the effectiveness of different UI representations, feedback mechanisms, and human intervention remains unclear. This paper presents a large-scale empirical study addressing these gaps. We evaluate nine state-of-the-art LLMs across 115 real-world apps, comparing three UI-context prompting settings: extracted textual context, UI-hierarchy XML, and screenshot-based vision input. Results show extracted context and XML achieve comparable page-pass-through rates of 71.4% and 71.0%, while vision-based input reaches 65.1% but incurs substantially higher token costs. In bug-detection experiments with 37 real-world text-input bugs, LLMs generating invalid inputs detect about 51% of issues across all evaluated models. A feedback-enhanced protocol, incorporating execution outcomes into subsequent attempts, improves average PPTRs to 69.2-73.8% and raises bug-detection rates to 51.0-64.5%. Human testers further refine inputs, yielding additional gains. We integrate the process into DroidBot, augmenting its UI-exploration capabilities. We derive actionable insights on context selection, cost-effectiveness, feedback strategies, and human-LLM collaboration, advancing both knowledge and practice in Android testing.