Dingmei Tan

Papers

Total Citations

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About

Dingmei Tan is a researcher specializing in software engineering and automated testing, with a particular focus on graphical user interface (GUI) element detection for mobile applications. Their most notable contribution is the development of YOLOv5-MGC, an improved object detection algorithm that addresses critical challenges in mobile app testing. This work, published in 2022 and garnering 17 citations, tackles the persistent problem of low accuracy and missed detection of tiny interface elements in GUI recognition—a fundamental bottleneck in automated testing pipelines. By enhancing the YOLOv5 architecture, Tan's research directly improves the reliability of automated mobile testing, which is essential for quality assurance in the rapidly expanding app ecosystem. Their work bridges computer vision and software engineering, offering practical solutions for identifying interface components that are often overlooked by conventional detection methods. Tan's contributions are particularly valuable for researchers and practitioners working on test automation, UI/UX validation, and mobile application quality, demonstrating how targeted algorithmic improvements can yield significant real-world impact in software testing workflows.