SMMM: A Metric Based Framework to Evaluate the Scalability of Multiple Model Methodologies

2018-06-29T19:27:05Z (GMT) by 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.