Hai‐Lei Ding

Papers

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About

Hai‐Lei Ding is a researcher at the forefront of automated software testing and robotics, with a specialized focus on enhancing user interface (UI) validation through advanced neural network techniques. His most notable contribution, "Comprehensive image restoration for robot-assisted PC-side UI automated testing using neural network" (2025), addresses a critical bottleneck in robotic testing: degraded or distorted visual inputs. By developing a neural network-based restoration framework, Ding enables robots to accurately interpret and interact with PC-side UIs even under challenging conditions—such as low resolution, noise, or partial occlusion—significantly improving the reliability of automated test suites. This work has already garnered early citations, signaling its immediate relevance to both academia and industry. Ding’s research bridges computer vision and software engineering, offering practical solutions that reduce manual testing effort and accelerate deployment cycles. His approach not only advances the robustness of robot-assisted testing but also opens new avenues for applying deep learning to real-world automation challenges. For students and researchers exploring the intersection of AI, robotics, and software quality assurance, Ding’s work exemplifies how targeted image restoration can transform the efficiency and accuracy of automated systems.