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.
Wil Aalst; John Mylopoulos; Michael Rosemann; Michael J. Shaw; Clemens Szyperski; Marten Sinderen; Paul Oude Luttighuis; Erwin Folmer; Steven Bosems. 5th International Working Conference on Enterprise Interoperability (IWEI), Mar 2013, Enschede, Netherlands. Springer, Lecture Notes in Business Information Processing, LNBIP-144, pp.104-117, 2013, Enterprise Interoperability. 〈10.1007/978-3-642-36796-0_10〉
https://hal.inria.fr/hal-01474204
Contributeur : Hal Ifip
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Soumis le : mercredi 22 février 2017 - 15:58:44
Dernière modification le : mercredi 22 février 2017 - 16:05:17
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Pontus Johnson, Johan Ullberg, Markus Buschle, Ulrik Franke, Khurram Shahzad. P2AMF: Predictive, Probabilistic Architecture Modeling Framework. Wil Aalst; John Mylopoulos; Michael Rosemann; Michael J. Shaw; Clemens Szyperski; Marten Sinderen; Paul Oude Luttighuis; Erwin Folmer; Steven Bosems. 5th International Working Conference on Enterprise Interoperability (IWEI), Mar 2013, Enschede, Netherlands. Springer, Lecture Notes in Business Information Processing, LNBIP-144, pp.104-117, 2013, Enterprise Interoperability. 〈10.1007/978-3-642-36796-0_10〉. 〈hal-01474204〉
