Automated Probabilistic System Architecture Analysis in the Multi-Attribute Prediction Language (MAPL): Iteratively Developed using Multiple Case Studies

Complex Systems Informatics and Modeling Quarterly

Pontus Johnson¹  Robert Lagerström¹  Mathias Ekstedt¹  Khurram Shahzad¹  Ulrik Franke² 

[1] KTH Royal Institute of Technology, Osquldas väg 12, 100 44 Stockholm;RISE Research Institutes of Sweden, SICS Swedish Institute of Computer Science, Kista;

关键词: System architecture;    architecture analysis;    system modeling;    probabilistic analysis.;   

DOI  :  10.7250/csimq.2017-11.03

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown