Making Sense of Software Engineering Data by Ensemble Methods: Opportunities and Challenges for Software Effort Estimation

Making Sense of Software Engineering Data by Ensemble Methods: Opportunities and Challenges for Software Effort Estimation

时间： 地点：九龙湖校区计算机学院三楼会议室

报告简介：

Developing effective software effort estimation (SEE) models based on software engineering data is an important research area, as correctly estimating the effort required to develop software is of vital importance for any software project manager. Over or underestimation of software development will lead to undesirable project outcomes. There are considerable amount of research carried out in this area and many of these newer models such as analogy-based estimation method provide an useful early estimate in the software development process as a viable technique for software project management. However, prediction performances generated from these models also depend heavily on the historical software project dataset characteristics as well as the selected model design combinations, as well as the context of the project being assessed. Resulting in unstable or conflicting performance results being generated. Until relative recently, our attempts and experimentations on the applications of multiple methods combining two or more single SEE methods have been successfully showing significant stability and performance improvements in the models produced, which is also in consistent with the findings from the machine learning research community. The important research direction provided in the study was that while there is no best single SEE method, there exist best combination of such SEE methods using Ensembles, because it has demonstrated the capacity to improve upon the accuracy of a single SEE model. While our previous study on ensembles has shined lights to the future development of SEE, in this seminar we will discuss what are the potentials and opportunities to apply Ensemble techniques to other software engineering application, and more importantly technical challenges within and areas to be further explored. Furthermore, we will also introduce other interesting research projects being carried out by our research group.

报告人简介：

Dr. Keung received his B.Sc in Computer Science from the University of Sydney, and his Ph.D in Software Engineering from the University of New South Wales, Australia. He worked as Research Scientist in the National ICT Australia Research Laboratory (NICTA) based in Sydney for a number of years prior to returning Hong Kong. He is an active software engineering researcher, and his main research interests are within Empirical Software Engineering, specifically in software effort and cost estimation, empirical modeling and evaluation of complex systems, and data-intensive ensemble methods for software engineering data. He has worked with a number of software companies and government departments across Australia and Asia Pacific and attracted funding support research projects, which had been successfully carried out. He has published papers in prestigious journals including IEEE-TSE, IEEE-SOFTWARE and many other leading journals and conferences in the field of Software Engineering.

计算机网络和信息集成教育部重点实验室（东南大学）