學萃講壇”秉承學名家風範、萃科技精華的理念，以學術為魂，以育人為本，追求技術創新，提升學術品位，營造濃郁學術氛圍，共品科技饕餮盛宴！
報告人：Dr. Xin Xia
報告題目：Bridging the Gap Between Software Engineering and Data Mining
報告地點：21#426（會議室）
報告時間：2018年12月28日9:30-11:30
主辦單位：科學技術研究院
承辦單位：伟徳国际官网登录入口
報告人簡介：Dr. Xin Xia is a lecturer (equivalent to U.S. assistant professor) at the Faculty of Information Technology, Monash University, Australia. Prior to joining Monash University, he was a post-doctoral research fellow in the software practices lab at the University of British Columbia in Canada, with a specialization in software analytics and mining software repositories. He got a Ph.D degree in June 2014 from College of Computer Science and Technology, Zhejiang University, China. He was a visiting student of Prof. David Lo in Singapore Management University. He has published 115 peer-reviewed papers at renowned journals and conferences such as IEEE Transactions on Software Engineering (TSE), IEEE Conference on Software Engineering (ICSE), IEEE/ACM Conference on Automated Software Engineering (ASE), etc. For details, please refer to his homepage: https://xin-xia.github.io/
報告簡介：Today, data miners often apply or extend data mining techniques to solve problems across many domains (e.g., social media, health informatics, and software systems); while domain experts leverage their own domain knowledge to solve their own problems. Data miners often apply their automated techniques to solve a wide range of problems across different domains with limited knowledge of the domain; while domain experts often have limited knowledge of automated techniques when solving their domain-specific problems.My research tries to bridge the gap between both types of experts (i.e., Data miners and Domain Experts). In this talk, I will focus on the software engineering domain and I will give an overview of several challenges facing data miner and domain experts as they make use of automated techniques, in particular: (1) although we have many easy-to-use data mining tools, many domain experts have limited knowledge of these tools, which often causes research bias; (2) strong performance of techniques is not sufficient, instead a deeper understanding of the domain is essential; (3) results should be presented in a domain-centric context . I will present examples from my research to explain what these challenges are, why do they appear, and my efforts to avoid them.
