Title: An Efficient and Scalable Approach to Correct Class Model Refinement
Dr. Wuwei Shen
Western Michigan University
Date: Oct 7, 2011
Room: 1345 EB
Host: Laura Dillon
Today, programmers benefit immensely from Integrated Development Environments (IDE) where errors are highlighted within seconds of their introduction. Yet, designers rarely benefit from such instant feedback in modeling tools. This paper focuses on the refinement of UML-style class models with instant feedback on correctness. We presume that engineers prefer to maintain high-level class models separately from low-level class models (or source code) for documentation or proof of correctness. However, this refinement and subsequent evolution lack automated support, let alone an instant feedback on the correctness of the refinement. Traditional approaches to consistency checking fail here because of the computational cost of comparing class models. Our instant approach first transforms the low-level model into an intermediate model that is then easier to compare with the high-level model. The key to computational scalability is the separation of transformation and comparison so that each can react optimally to changes in the design. We evaluate our approach on eight third-party design models. The empirical data show that the separation of transformation and comparison results in a 6- to 11-fold performance gain and a 9-fold reduction in producing irrelevant feedback.
Wuwei Shen is an associate professor at Western Michigan University. Dr. Shen received a PhD in Computer Science from Department of EECS, the University of Michigan in 2002. His main research interests include the software development process, CASE tools, UML, and the UML-based model testing.
He is a member of IEEE and ACM.