By Topic

An Efficient and Scalable Approach to Correct Class Model Refinement

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Wuwei Shen ; Western Michigan University, Kalamazoo ; Kun Wang ; Alexander Egyed

Today, programmers benefit immensely from Integrated Development Environments (IDEs), where errors are highlighted within seconds of their introduction. Yet, designers rarely benefit from such an instant feedback in modeling tools. This paper focuses on the refinement of UML-style class models with instant feedback on correctness. Following the Model-Driven Architecture (MDA) paradigm, we strongly believe in the benefit of maintaining high-level and low-level models separately to 1) document the lower level model and 2) continuously ensure the correctness of the low-level model during later evolution (i.e., high- or low-level models may be evolved independently). However, currently the refinement and subsequent evolution lack automated support, let alone an instant feedback on their correctness (i.e., consistency). Traditional approaches to consistency checking fail here because of the computational cost of comparing class models. Our proposed instant approach first transforms the low-level model into an intermediate model that is then easier comparable 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-changes that could happen concurrently in both the high- and low-level class models. 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 ninefold reduction in producing irrelevant feedback. While this work emphasizes the refinement of class models, we do believe that the concepts are more generally applicable to other kinds of modeling languages, where transformation and subsequent comparison are computationally expensive.

Published in:

IEEE Transactions on Software Engineering  (Volume:35 ,  Issue: 4 )