PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing

TL;DR

PopSweeper tackles the disruption caused by app-blocking pop-ups in automated mobile GUI testing by providing a real-time, vision-based solution. It combines a two-stage pop-up classifier (ResNet50 followed by MobileNetV2) with a YOLO-World close-button detector, integrated into a histogram-difference-based real-time screenshot pipeline. The approach is validated on Rico-derived and top-app datasets, achieving a classification F1 of 0.926 and close-button BoxAP of 0.939, while end-to-end tests show 87.1% app-wide blockage resolution with around 60 ms per frame overhead. A public dataset and code accompany the work, underscoring its practical impact for improving reliability and efficiency in automated GUI testing without requiring instrumentation.

Abstract

Graphical User Interfaces (GUIs) are the primary means by which users interact with mobile applications, making them crucial to both app functionality and user experience. However, a major challenge in automated testing is the frequent appearance of app-blocking pop-ups, such as ads or system alerts, which obscure critical UI elements and disrupt test execution, often requiring manual intervention. These interruptions lead to inaccurate test results, increased testing time, and reduced reliability, particularly for stakeholders conducting large-scale app testing. To address this issue, we introduce PopSweeper, a novel tool designed to detect and resolve app-blocking pop-ups in real-time during automated GUI testing. PopSweeper combines deep learning-based computer vision techniques for pop-up detection and close button localization, allowing it to autonomously identify pop-ups and ensure uninterrupted testing. We evaluated PopSweeper on over 72K app screenshots from the RICO dataset and 87 top-ranked mobile apps collected from app stores, manually identifying 832 app-blocking pop-ups. PopSweeper achieved 91.7% precision and 93.5% recall in pop-up classification and 93.9% BoxAP with 89.2% recall in close button detection. Furthermore, end-to-end evaluations demonstrated that PopSweeper successfully resolved blockages in 87.1% of apps with minimal overhead, achieving classification and close button detection within 60 milliseconds per frame. These results highlight PopSweeper's capability to enhance the accuracy and efficiency of automated GUI testing by mitigating pop-up interruptions.

Figures (4)

• Figure 1: An illustrative example on the impact of app-blocking pop-ups on automated testing.
• Figure 2: Three types of pop-ups: full-screen, partially overlay, and banners. Red boxes mark the pop-ups and corresponding close buttons.
• Figure 3: An overview of PopSweeper.
• Figure 4: Examples of app-blocking pop-up misclassification.