Papers
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Total Citations
17
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About
Aodi Wei is a researcher focused on advancing automated testing and user interface analysis for mobile applications, with a particular emphasis on improving object detection for graphical user interfaces (GUIs). Their most cited work, "YOLOv5-MGC: GUI Element Identification for Mobile Applications Based on Improved YOLOv5" (2022, 17 citations), tackles a critical bottleneck in mobile app testing: the low accuracy and missed detection of tiny GUI elements by existing algorithms. By enhancing the YOLOv5 architecture, Wei introduced a more robust method for identifying interface components, directly enabling smoother and more reliable automated testing pipelines. This contribution is vital for software quality assurance, as accurate element recognition is the foundational step for any GUI-driven test automation. Wei’s work bridges computer vision and software engineering, offering practical improvements that reduce false negatives in real-world mobile applications. With 17 citations, this paper has already influenced subsequent research in UI testing and object detection, marking Wei as a promising voice in the intersection of deep learning and mobile app reliability. Their research continues to support the development of more intelligent, efficient testing frameworks.
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