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SMMM: A Metric Based Framework to Evaluate the Scalability of Multiple Model Methodologies
Any type of content formally published in an academic journal, usually following a peer-review process.
posted on 01.01.2000by Akhilesh Bajaj
Multiple Model Methodologies (MMM) have become ubiquitous in the area of conceptual
modeling. Thus, the Unified Modeling Language (UML) is a popular MMM in software
engineering, while MMMs are also used in enterprise modeling. Over the last few years, the size
problem domains being modeled by MMMs has also grown. Thus, software is now bigger, and
enterprises are significantly larger in scale than the problem domains modeled when building the
legacy systems. These trends in the area of conceptual modeling raise an important question
about the scalability of MMMs, as they are applied to domains of increasing size. In this work,
we present a comprehensive, metric based framework called SMMM (Scalability of MMM).
SMMM assumes that the only obstacle to the scalability of an MMM is the complexity that users
face when using the MMM to create models of the underlying reality, as this reality grows in size.
SMMM extends previous work in the area of complexity measurement in the following ways.
First, SMMM is comprehensive, and yet parsimonious. Second, metrics in earlier works have
made explicit assumptions about the relative cognitive difficulties of modeling different
categories of concepts. SMMM makes no assumptions about any concept being harder to model
than another. Third, previous work on metric development has omitted the role of empirical work
in understanding complexity. The SMMM framework explicitly recognizes the role of empirical
work in evaluating cognitive difficulty. Fourth, SMMM measures both intra-model and intermodel
complexity. Intra-model complexity values show which models in the MMM are the
source of complexity. Inter-model complexity values measure the complexity by the interaction
between different models in the MMM.