P~2AMF: Predictive, Probabilistic Architecture Modeling Framework

摘要

In the design phase of business and software system development, it is desirable to predict the properties of the system-to-be. Existing prediction systems do, however, not allow the modeler to express uncertainty with respect to the design of the considered system. In this paper, we propose a formalism, the Predictive, Probabilistic Architecture Modeling Framework (P~2AMF), capable of advanced and probabilistically sound reasoning about architecture models given in the form of UML class and object diagrams. The proposed formalism is based on the Object Constraint Language (OCL). To OCL, P~2AMF adds a probabilistic inference mechanism. The paper introduces P~2AMF, describes its use for system property prediction and assessment, and proposes an algorithm for probabilistic inference.