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System-of-systems(So S) engineering involves a complex process of refining high-level So S requirements into more detailed systems requirements and assessing the extent to which the performances of to-be systems may possibly satisfy So S capability objectives. The key issue is how to model such requirements to automate the process of analysis and assessment. This paper suggests a meta-model that defines both functional and nonfunctional features of So S requirements for command and control,communication, computer, intelligence, surveillance reconnaissance(C4ISR) systems. A domain-specific modeling language is defined by extending unified modeling language(UML) constructed of class and association with fuzzy theory in order to model the fuzzy concepts of performance requirements. An efficiency evaluation function is introduced, based on B ′ezier curves,to predict the effectiveness of systems. An algorithm is presented to transform domain models in fuzzy UML into a requirements ontology in description logic(DL) so that requirements verification can be automated with a popular DL reasoner such as Pellet.
System-of-systems (So S) engineering involves a complex process of refining high-level So S requirements into more detailed systems requirements and assessing the extent to which the performances of to-be systems may substantially satisfy So S capability objectives. The key issue is how to model such requirements to automate the process of analysis and assessment. This paper suggests a meta-model that defines both functional and nonfunctional features of So S requirements for command and control, communication, computer, intelligence, surveillance reconnaissance (C4ISR) systems. A domain-specific modeling language is defined by extending unified modeling language (UML) constructed of class and association with fuzzy theory in order to model the fuzzy concepts of performance requirements. An efficiency evaluation function is introduced, based on B ’ezier curves , to predict the effectiveness of systems. An algorithm is presented to transform domain models in fuzzy UML into a requirements ontolog y in description logic (DL) so that requirements verification can be automated with a popular DL reasoner such as Pellet.