时间：2017年5月2日（星期二）上午9：30
地点：旗山校区软件学院512
主讲：南方科技大学（英国伯明翰大学）姚新教授
主办：软件学院
专家简介：Xin Yao is a part (Professor) of Computer Science and the Director of CERCIA(Centre of Excellence for Research in Computational Intelligence andApplications) at the University of Birmingham, UK. He is a chair professorship at the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China, in Fall 2016. He is an IEEE Fellow andthe President (2014-15) of IEEE Computational Intelligence Society (CIS).He won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010 IEEE Transactionson Evolutionary Computation Outstanding Paper Award, 2010 BT Gordon RadleyAward for Best Author of Innovation (Finalist), 2011 IEEE Transactions onNeural Networks Outstanding Paper Award, and many other best paper awards.He won the prestigious Royal Society Wolfson Research Merit Awardin 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013.His major research interests include evolutionary computation, ensemblelearning, and real-world applications.
报告摘要：Computational intelligence has been used in software engineering for a long time. There has been a recent surge in interest in this area, especially in search-based software engineering. This talk touches upon some of the recent examples in the broader field of computational intelligence in software engineering. It is highlighted that software engineering could benefit fromadvanced computational intelligence techniques in tackling hard problems,e.g., software module clustering, software reliability maximisation, software project scheduling, software effort estimation, software defect prediction, etc. It is also argued that new research challenges posed by software engineering could stimulate further development of new theories and algorithms in computational intelligence. Such theoretical research could shed some light on important research issues and provide guidance in future work. For example, theoretical analysis of computational time complexity of search algorithms can inform us about the limitation of search-based software engineering. The research in online learning algorithms can help us develop novel approaches to software effort estimation when historical data within a company are sparse. The primary aim of this talk is not to provide a comprehensive review of computational intelligence for software engineering, but to illustrate the opportunities for further research and development in this area through selected examples.