Model replication: transformations to address model scalability

  • Authors:

  • Yuehua Lin;Jeff Gray;Jing Zhang;Steve Nordstrom;Aniruddha Gokhale;Sandeep Neema;Swapna Gokhale

  • Affiliations:

  • Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, AL 35294-1170, U.S.A.;Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, AL 35294-1170, U.S.A.;Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, AL 35294-1170, U.S.A.;Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN 37235, U.S.A.;Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN 37235, U.S.A.;Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN 37235, U.S.A.;Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, U.S.A.

  • Venue:
  • Software—Practice & Experience
  • Year:
  • 2008

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Abstract

In model-driven engineering, it is often desirable to evaluate different design alternatives as they relate to scalability issues of the modeled system. A typical approach to address scalability is model replication, which starts by creating base models that capture the key entities as model elements and their relationships as model connections. A collection of base models can be adorned with necessary information to characterize a specific scalability concern as it relates to how the base modeling elements are replicated and connected together. In current modeling practice, such a model replication is usually accomplished by scaling the base model manually. This is a time-consuming process that represents a source of error, especially when there are deep interactions between model components. As an alternative to the manual process, this paper presents the idea of automated model replication through a model transformation process that expands the number of elements from the base model and makes the correct connections among the generated modeling elements. The paper motivates the need for model replication through case studies taken from models supporting different domains. Copyright © 2008 John Wiley & Sons, Ltd.