Fei Shao
Papers
1
Total Citations
11
H-Index
1
About
Fei Shao’s research lies at the intersection of web engineering, data extraction, and software evolution, with a focus on ensuring the long-term reliability of automated web systems. His most-cited work, “WebEvo: taming web application evolution via detecting semantic structure changes” (2021, 11 citations), addresses a critical challenge in the age of Big Data: as websites continuously evolve, information retrieval and robotic process automation tools often break. Shao’s contribution is a method that detects semantic structure changes in web pages, enabling these tools to adapt rather than fail. This work has practical implications for industries relying on large-scale web data extraction, from e-commerce to finance. Beyond this, Shao’s research demonstrates a deep understanding of how dynamic web environments impact automated processes, offering solutions that bridge the gap between web development and data science. His work is a valuable resource for students and researchers interested in building resilient web automation systems, and his citation record reflects growing recognition of his contributions to taming the complexity of web evolution.
Research Focus
Key Achievements
Top Papers
- 1