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报告人：Dr. Xin Xia
报告题目：Bridging the Gap Between Software Engineering and Data Mining
报告地点：21#426（会议室）
报告时间：2018年12月28日9:30-11:30
主办单位：科学技术研究院
承办单位：澳门十大网赌正规官网
报告人简介：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/
报告简介：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.
