PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing
Journal:
arXiv
Published Date:
Dec 4, 2024
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.
