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Abstract: System of systems engineering (SoSE) involves the complex procedure of translating capability needs into the high-level requirements for system of systems (SoS) and evaluating how the SoS quality requirements meet their capability needs. One of the key issues is to model the SoS requirements and automate the verification procedure. To solve the problem of modeling and verification, meta-models are proposed to refine both functional and non-functional characteristics of the SoS requirements. A domain-specific modeling language is defined by extending Unified Modeling Language (UML) class and association with fuzzy constructs to model the vague and uncertain concepts of the SoS quality requirements. The efficiency evaluation function of the cloud model is introduced to evaluate the efficiency of the SoS quality requirements. Then a concise algorithm transforms the fuzzy UML models into the description logic (DL) ontology so that the verification can be automated with a DL reasoner. This method implements modeling and verification of high-level SoS quality requirements. A crisp case is used to facilitate and demonstrate the correctness and feasibility of this method.

This research paper proposes an interesting approach within the Research area of SoSE, although the applicability and feasibility of this approach remain unclear. The paper is well structured and presented.

一种基于描述逻辑的体系质量需求建模与验证方法

[1]Ahmed, R., Robinson, S., 2007. Simulation in business and industry: how simulation context can affect simulation practice Proc. Spring Simulation Multiconference, p.152-159.

[2]Bagdatli, B., Mavris, D., 2012. Use of high-level architecture discrete event simulation in a system of systems design. IEEE Aerospace Conf., p.1-13.

[3]Chapurlat, V., Braesch, C., 2008. Verification, validation, qualification and certification of enterprise models: statements and opportunities. Comput. Ind., 59(7):711-721.

[4]Chapurlat, V., Kamsu-Fogum, B., Prunet, F., 2006. A formal verification framework and associated tools for enterprise modeling: application to UEML. Comput. Ind., 57(2): 153-166.

[5]Dong, Q.C., Wang, Z.X., Chen, G.Y., et al., 2012. Domain-specific modeling and verification for C4ISR capability requirements. J. Cent. South Univ., 19(5):1334-1341.

[6]Ender, T., Leurck, R.F., Weaver, B., et al., 2010. Systems of systems analysis of ballistic missile defense architecture effectiveness through surrogate modeling and simulation. IEEE Syst. J., 4(2):156-166.

[7]Eusgeld, I., Nan, C., Dietz, S., 2011. “System-of-systems” approach for interdependent critical infrastructures. Reliab. Eng. Syst. Safety, 96(6):679-686.

[8]Fernando, B., Miguel, D., Juan, G.R., 2009. Fuzzy description logics under Godel semantics. Int. J. Approx. Reas., 50(3):494-514.

[9]Fotrousi, F., Fricker, S.A., Fiedler, M., 2014. Quality requirements elicitation based on inquiry of quality-impact relationships. 22nd IEEE Int. Requirements Engineering Conf., p.303-312.

[10]Gao, J.X., Li, D.Q., Havlin, S., 2014. From a single network to a network of networks. Nat. Sci. Rev., 1(3):346-356.

[11]Ge, B.F., Hipel, K.W., Yang, K.W., et al., 2013. A data-centric capability focused approach for system-of-systems architecture modeling and analysis. Syst. Eng., 16(3):363-377.

[12]Ge, B.F., Hipel, K.W., Fang, L.P., et al., 2014a. An interactive portfolio decision analysis approach for system of systems architecting using the graph model for conflict resolution. IEEE Trans. Syst. Man Cybern., 44(10):1328-1346.

[13]Ge, B.F., Hipel, K.W., Yang, K.W., et al., 2014b. A novel executable modeling approach for system of systems architecture. IEEE Syst. J., 8(1):4-13.

[14]Grigoroudis, E., Phillis, Y.A., 2013. Modeling healthcare system of systems: a mathematical programming approach. IEEE Syst. J., 7(4):571-580.

[15]Guizzardi, G., 2005. Ontological Foundations for Structural Conceptual Models. PhD Thesis, Centre for Telematics and Information Technology, University of Twente, Enschede, the Netherlands.

[16]Haimes, Y.Y., 2012. Modeling complex systems of systems with phantom system models. Syst. Eng., 15(3):333-346.

[17]Holt, J., Perry, S., Payne, R., 2015. A model-based approach for requirements engineering for systems of systems. IEEE Syst. J., 9(1):252-262.

[18]Huynh, T.V., Kessler, A., Oravec, J., 2011. Orthogonal array experiment in systems engineering and architecting. Syst. Eng., 14(2):208-222.

[19]Khan, I., 2010. Methodology for the development of executable system architecture. Proc. 8th Int. Conf. on FIT, p.1-4.

[20]Ma, Z.M., Zhang, F., Cheng, J., et al., 2011. Representing and reasoning on fuzzy UML models: a description logic approach. Expert Syst. Appl., 38(3):2536-2549.

[21]Ma, Z.M., Li, Y., Zhang, F., 2012. Modeling fuzzy information in UML class diagrams and object-oriented database models. Fuzzy Sets Syst., 186(1):26-46.

[22]Moynihan, R.A., Reining, R.C., Salamone, P.P., et al., 2009. Enterprise scale portfolio analysis at the National Oceanic and Atmospheric Administration (NOAA). Syst. Eng., 12(2):155-168.

[23]Ncube, C., Lim, S.L., Dogan, H., 2013. Identifying top challenges for international research on requirements engineering for systems of systems engineering. 21st IEEE Int. Requirements Engineering Conf., p.342-344.

[24]Office of the Deputy Under Secretary of Defense for Acquisition and Technology and Logistics (ODUSD (A&T)), 2008. Systems and Software Engineering, Systems Engineering Guide for Systems of Systems, Version 1.0. Technical Report No. ODUSD (A&T)SSE, Washington, D.C., USA.

[25]Petersen, K., Khurum, M., Angelis, L., 2014. Reasons for bottlenecks in very large-scale system of systems development. Inform. Softw. Techol., 56(10):1403-1420.

[26]Regnell, B., Svensson, R.B., Olsson, T., 2008. Supporting roadmapping of quality requirements. IEEE Softw., 25(2):42-47.

[27]Stoilos, G., Stamou, G., Pan, J.Z., et al., 2007. Reasoning with very expressive fuzzy description logics. J. Artif. Intell. Res., 30:273-320.

[28]Wang, R., Dagli, C.H., 2011. Executable system architecting using systems modeling language in conjunction with colored Petri nets in a model driven systems development process. Syst. Eng., 14(4):383-409.