Data-flow based model analysis and its applications

Abstract

In this paper we present a data-flow based approach to static model analysis to address the problem of current methods being either limited in their expressiveness or employing formalisms which complicate seamless integration with standards and tools in the modeling domain. By applying data-flow analysis - a technique widely used for static program analysis - to models, we realize what can be considered a generic "programming language" for context-sensitive model analysis through declarative specifications. This is achieved by enriching meta models with data-flow attributes which are afterward instantiated for models. The resulting equation system is subjected to a fixed-point computation that yields a static approximation of the model's dynamic behavior as specified by the analysis. The applicability of the approach is evaluated in the context of a running example, the examination of viable application domains and a statistical review of the algorithm's performance. © 2013 Springer-Verlag.