Yung-Pin Cheng

Papers

Total Citations

H-Index

About

Yung-Pin Cheng is a researcher whose work bridges computer vision, software engineering, and industrial automation, with a particular focus on transforming GUI testing and human-machine interaction. His most-cited paper, “Apply computer vision in GUI automation for industrial applications” (2019, 13 citations), addresses a critical gap in Industry 4.0: while physical labor is increasingly automated, many workers remain tethered to desktop software interfaces. Cheng proposes using computer vision to automate GUI interactions, enabling smarter, more flexible automation in industrial settings. This work exemplifies his broader contributions to making software testing more intelligent and less reliant on brittle, script-based approaches. By integrating vision-based techniques into GUI automation, Cheng helps reduce manual testing burdens and improve reliability in complex industrial systems. His research is particularly valuable for practitioners seeking to modernize legacy software workflows. Though his citation count is modest, his work is notable for its practical, application-driven focus—directly addressing real-world challenges in smart manufacturing and human-computer interaction. For students and researchers interested in the intersection of computer vision, software testing, and industrial automation, Cheng’s work offers a compelling, hands-on perspective on how academic research can directly impact industry practices.