PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing
Linqiang Guo, Wei Liu, Yi Wen Heng, Tse-Hsun (Peter) Chen, Yang Wang

TL;DR
PopSweeper is a real-time tool that uses deep learning to detect and resolve app-blocking pop-ups during automated mobile GUI testing, significantly improving test reliability and efficiency.
Contribution
We introduce PopSweeper, a novel deep learning-based system that automatically detects and resolves app-blocking pop-ups in real-time during automated testing.
Findings
Achieved 91.7% precision and 93.5% recall in pop-up detection.
Successfully resolved blockages in 87.1% of tested apps.
Operates within 60 milliseconds per frame, enabling real-time performance.
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…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Mobile and Web Applications · Software Testing and Debugging Techniques
