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Verification and validation principles from a systems perspective

Murray-Smith, D. J. (2019) Verification and validation principles from a systems perspective. In: Beisbart, C. and Saam, N. J. (eds.) Computer Simulation Validation: Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Series: Simulation foundations, methods and Applications. Springer: Cham, Switzerland, pp. 99-118. ISBN 9783319707655 (doi: 10.1007/978-3-319-70766-2_4)

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Abstract

This chapter introduces concepts and principles associated with the verification and validation of simulation models, mainly in the context of models of complete systems from a systems-modelling perspective. The word “verification” is used here to describe testing processes to establish whether a computer-based representation correctly describes the underlying mathematical, logical and theoretical structure of the model. The word “validation” is used to describe procedures for establishing whether the model fidelity is adequate for the purposes of the given application. Verification is internal to the model and the computer-based representation while validation processes involve information external to the model, normally using data or observations from the corresponding real system. The goal of the testing process for a simulation model must always be to establish the extent to which a model has the quality and credibility required for the intended application. These model testing processes, involving both verification and validation, are inherently iterative.

Item Type:	Book Sections
Keywords:	Continuous system simulation models, testing, sensitivity, identifiability, documentation, model acceptance, model upgrading.
Status:	Published
Glasgow Author(s) Enlighten ID:	Murray-Smith, Professor David
Authors:	Murray-Smith, D. J.
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General)
College/School:	College of Science and Engineering > School of Engineering
Publisher:	Springer
ISBN:	9783319707655
Published Online:	10 April 2019
Related URLs:	Publisher

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References

Anderson, J., & Papachristodoulou, A. (2009). On validation and invalidation of biological models BMC Bioinfomatics, 10(1), 132. Beck, J.V., & Arnold K.J. (1977). Parameter estimation in science and engineering. New York, NY: Wiley. Bellman, R., & Åstrӧm, K.J., (1970). On structural identifiability. Mathematical Biosciences, 7, 329-339. Bradley, R., Padfield, G.D., Murray-Smith, D.J., et al. (1990). Validation of helicopter mathematical models. Transactions of the Institute of Measurement and Control, 12, 186-196. Butterfield, M.H., &Thomas, P.J. (1986). Methods of quantitative validation for dynamic system models – part 1: theory. Transactions of the Institute of Measurement & Control, 8, 182-200. Caro, J.J., Briggs, A.H., Siebert, U. et al. (2012). Modeling good research practices – overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force -1. Medical Decision Making, 32, 667-677. Chatfield, C. (2003). The analysis of time series: an introduction, 6th edn., Boca Raton, FL: Chapman and Hall/CRC. Cloud, D.J., & Rainey L.B., (Eds.) (1998) Applied modelling and simulation: an integrated approach to development and operation. New York, NY: McGraw-Hill. Eddy, D.M., Hollingsworth, W., Caro, J.J., et al. (2012). Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force -7. Medical Decision Making, 32, 733-743. Frank, P.M. (1978) An introduction to system sensitivity theory, London, UK: Academic. Gábor, A., Villaverde, A.F. & Banga, J.R. (2017). Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems. BMC Systems Biology;11: 54. doi: 10.1186/s12918-017-0428-y Goodwin, G.C., & Payne, R.L. (1977). Dynamic system identification: experiment design and data analysis. New York, NY: Academic. Gore, R., & Diallo, S. (2013). The need for usable formal methods in verification and validation. In: R. Pasupthy, S.-H. Kim, A. Tolk et al. (Eds.) Proceedings of the 2013 winter simulation conference (pp 1257-1268). Washington DC: IEEE. doi: 10.1109/WSC.2013.6721513 Grewal, M.S., & Glover, K. (1976). Identifiability of linear and nonlinear dynamical systems. IEEE Transactions on Automatic Control 21, 833-837. Hamby, D.M. (1994). A review of techniques for parameter sensitivity analysis of environmental models. Environmental Monitoring and Assessment 32, 135-154, Heitmeyer, C.L. (2007). Formal methods for specifying, validating and verifying requirements. Journal of Universal Computer Science. 13(5), 607-618. Hemez, F.M. (2004) The myth of science-based predictive modelling. In: Proceedings foundtions ’04 workshop for verification, validation and accreditation (VV&A) in the 21st century. Arizona State University, Tempe, Arizona, 13-15 October 2004. Report LA-UR-04-6829, Los Alamos National Laboratory, USA Heyhurst, K.L., Veerhusen, D.S., Chilenski, J.L. et al. (2001) A practical tutorial on modified condition/decision coverage, NASA/TM-2001-210876, Hampton, VA, USA: National Aeronautics and Space Administration, Langley Research Center. Kaner, C., Falk, J., & Nguyen, H.Q. (1999). Testing computer software (2nd Edition), New York, NY: Wiley. Kit, E. (1995) Software testing in the real world, Harlow, UK: Addison Wesley. Kuhn, D.R., Chandramouli, R., & Butler, R.W. (2002). Cost effective use of formal methods in verification and validation. Invited paper, Presented at Foundations ’02 Workshop, US Department of Defense, Laurel, Maryland, October 22-23, 2002. Retrieved from http://csrc.nist.gov/staff/Kuhn/kuhn-chandramouli-butler-02.pdf . Mitre systems engineering guide: Verification and validation of simulation models (2014). pp 461-469. Bedford, MA: The Mitre Corporation. www.mitre.org/publications/technical-papers/the-mitre-systems-engineering-guide. Murray-Smith, D.J. (2015) Testing and validation of computer simulation models: principles, methods and applications. Cham, Switzerland: Springer. Oberkampf, W.L. & Roy, C. J. (2010), Verification and validation in scientific computing. Cambridge: Cambridge University Press. Oreskes, N., Shrader-Frechette, K. & Belitz, K. (1994). “Verification, validation and confirmation of numerical models in the earth sciences,” Science. 263(5147): 641–646. Pace, D.K. (2004). Modeling and simulation verification and validation challenges, Johns Hopkins APL Technical Digest. 25,163-172. Pranja, S. (2006). Barrier certificates for nonlinear model validation. Automatica. 42:117-126. Raue, A, Kreutz, C., Maiwald, T. et al. (2009). Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics. 25, 1923–1929. Schlacher, K., & Schöӧberl, M. (Eds.) (2011). Special issue; Modelling, analysis and control of distributed parameter systems. Mathematical and Computer Simulation of Dynamical Systems. 17, 1-121. Smith, M.I., Murray-Smith, D.J., & Hickman D. (2007). Verification and validation issues in a generic model of an electro-optic sensor system. Journal of Defense Modeling & Simulation. 4, 17-27. Steinberg, S., & Roache, P.J. (1985). Symbolic manipulation and computational fluid dynamics, Journal of Computational Physics. 57, 251-284. Tomović, R. (1963). Sensitivity analysis of dynamic systems. New York, NY; McGraw-Hill. Winsberg, E. (2018), Computer Simulations in Science, The Stanford Encyclopedia of Philosophy (Summer 2018 Edition), Edward N. Zalta (ed.), forthcoming URL = <https://plato.stanford.edu/archives/sum2018/entries/simulations-science/>.

Deposit and Record Details

ID Code:	189835
Depositing User:	Professor David Murray-Smith
Datestamp:	08 Jul 2019 09:01
Last Modified:	21 Dec 2021 12:20
Date of first online publication:	10 April 2019