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P2AMF: Predictive, Probabilistic Architecture Modeling Framework

  • Pontus Johnson
  • Johan Ullberg
  • Markus Buschle
  • Ulrik Franke
  • Khurram Shahzad
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 144)

Abstract

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 (P2AMF), 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, P2AMF adds a probabilistic inference mechanism. The paper introduces P2AMF, describes its use for system property prediction and assessment, and proposes an algorithm for probabilistic inference.

Keywords

probabilistic inference system properties prediction Object Constraint Language UML class diagram object diagram 

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Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Pontus Johnson
    • 1
  • Johan Ullberg
    • 1
  • Markus Buschle
    • 1
  • Ulrik Franke
    • 1
    • 2
  • Khurram Shahzad
    • 1
  1. 1.Industrial Information and Control SystemsKTH Royal Institute of TechnologyStockholmSweden
  2. 2.FOI - Swedish Defence Research AgencyStockholmSweden

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