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Automated Probabilistic System Architecture Analysis in the Multi-Attribute Prediction Language (MAPL): Iteratively Developed using Multiple Case Studies
KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering. (Software Systems Architecture & Security)ORCID iD: 0000-0003-3089-3885
KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering. (Software Systems Architecture & Security)
KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering. (Software Systems Architecture & Security)
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2017 (English)In: International Journal of Complex Systems Informatics and Modeling Quarterly (CSIMQ), Vol. June/July, no 11, p. 38-68Article in journal (Refereed) Published
Abstract [en]

The Multi-Attribute Prediction Language (MAPL), an analysis metamodel for non-functional qualities of system architectures, is introduced. MAPL features automate analysis in five non-functional areas: service cost, service availability, data accuracy, application coupling, and application size. In addition, MAPL explicitly includes utility modeling to make trade-offs between the qualities. The article introduces how each of the five non-functional qualities are modeled and quantitatively analyzed based on the ArchiMate standard for enterprise architecture modeling and the previously published Predictive, Probabilistic Architecture Modeling Framework, building on the well-known UML and OCL formalisms. The main contribution of MAPL lies in the probabilistic use of multi-attribute utility theory for the trade-off analysis of the non-functional properties. Additionally, MAPL proposes novel model-based analyses of several non-functional attributes. We also report how MAPL has iteratively been developed using multiple case studies.

Place, publisher, year, edition, pages
2017. Vol. June/July, no 11, p. 38-68
Keywords [en]
System architecture; architecture analysis; system modeling; probabilistic analysis.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-221066DOI: 10.7250/csimq.2017-11.03OAI: oai:DiVA.org:kth-221066DiVA, id: diva2:1173090
Note

QC 20180115

Available from: 2018-01-11 Created: 2018-01-11 Last updated: 2018-01-15Bibliographically approved

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CiteExportLink to record
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  • apa
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  • de-DE
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  • Other locale
More languages
Output format
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  • asciidoc
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