@Article{dblp:journals/tse/ShenWE09,
  author    = {Wuwei Shen and Kun Wang and Alexander Egyed},
  title     = {An Efficient and Scalable Approach to Correct Class Model Refinement.},
  journal   = {IEEE Transactions on Software Engineeering},
  year      = {2009},
  volume    = {35},
  number    = {4},
  pages     = {515-533},
  abstract  = {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.},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl    = {https://dblp.org/rec/bib/journals/tse/ShenWE09},
  file      = {:Journals\\TSE 2009 - An Efficient and Scalable Approach to Correct Class Model Refinement\\An Efficient and Scalable Approach to Correct Class Model Refinement-preprint.pdf:PDF},
  keywords  = {FWF P21321},
  url       = {http://doi.ieeecomputersociety.org/10.1109/TSE.2009.26},
}