Abstract
Verification activities provide the evidence of contractual fulfillment in the engineering of systems. Thus, the importance of adequately defining verification activities in any acquisition program is unquestionable. Its significance extends beyond contracting though. A major portion of the development financial budget is spent in executing verification activities, and verification activities are the main vehicle in discovering knowledge about the system, which is key to reduce development risk. Hence, it is important to optimize verification activities so that a given level of confidence about the proper functioning of the system is achieved with minimum investment. Current approaches to optimize verification strategies assume that a verification activity contributes to an absolute increase or decrease in such confidence. That means that the confidence generated by a verification activity is independent of the past or potential future results of other verification activities. However, this representation is not an accurate reflection of actual practice. On the contrary, the necessity to perform a given verification activity depends on the results of all verification activities that have been previously performed. In order to address such limitation, we show in this paper how Bayesian networks can be an effective approach to capture the information dependencies of verification activities.
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References
Engel, A. (2010). In A. P. Sage (Ed.), Verification, validation, and testing of engineered systems, Wiley series on systems engineering and management. Hoboken: Wiley.
Salado, A. (2015). Defining better test strategies with tradespace exploration techniques and pareto fronts: Application in an industrial project. Systems Engineering, 18(6), 639–658.
Salado, A. (2016). Integrating design and verification decisions in value-driven design to increase system’s expected value. In Conference on Systems Engineering Research. Huntsville.
Salado, A. (2016). Applying tradespace exploration to verification engineering: From practice to theory and back again. In Conference on Systems Engineering Research (CSER). Huntsville.
INCOSE. (2015). Systems engineering handbook: A guide for system life cycle processes and activities (version 4.0 ed.). Hoboken: Wiley.
Neapolitan, R. E. (2004). Learning Bayesian networks. Upper Saddle River: Pearson Prentice Hall.
Grover, J. (2013). A literature review of Bayes’ theorem and Bayesian belief networks (BBN). In Strategic economic decision-making (pp. 11–27). New York: Springer.
Aalders, I., Hough, R., & Towers, W. (2011). Risk of erosion in peat soils–an investigation using Bayesian belief networks. Soil Use and Management, 27(4), 538–549.
Austin, M. F., Homberger, C., Ahalt, V., Doolittle, E., Polacek, G. A., & York, D. M. (2017) Applying Bayesian networks to TRL assessments: Innovation in systems engineering. In INCOSE International Symposium (Vol. 27, No. 1, pp. 1622–1634).
Austin, M. F., Homberger, C., Polacek, G. A., Doolittle, E., Ahalt, V., & York, D. M. (2018). Using Bayesian networks to validate technology readiness assessments of systems. Cham: Springer.
Costa, A. N., & Costa, P. C. G. (2018). Simulation-based air mission evaluation with Bayesian threat assessment for opposing forces. Cham: Springer.
Wertz, J. R., & Larson, W. J. (1999). Space mission analysis and design. Dordrecht: Microcosm.
ECSS. (2009). Space engineering: Verification. Noordwijk: European Cooperation for Space Standardization.
Larson, W. J., Kirkpatrick, D., Sellers, J., Thomas, L., & Verma, D. (2009). Applied space systems engineering, Space technology series. New York: McGraw Hills.
Collopy, P. D., & Hollingsworth, P. M. (2011). Value-driven design. Journal of Aircraft, 48(3), 749–759.
Salado, A., & Kannan, H. (2018). A mathematical model of verification strategies. Systems Engineering 21(6), 593–608.
Acknowledgment
This material is based upon the work supported by the Naval Postgraduate School Acquisition Research Program under Grant No.N00244-17-1-0013. The views expressed in written materials or publications, and/or made by speakers, moderators, and presenters, do not necessarily reflect the official policies of the Naval Postgraduate School nor does mention of trade names, commercial practices, or organizations imply endorsement by the US Government.
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Salado, A., Kannan, H., Farkhondehmaal, F. (2019). Capturing the Information Dependencies of Verification Activities with Bayesian Networks. In: Adams, S., Beling, P., Lambert, J., Scherer, W., Fleming, C. (eds) Systems Engineering in Context. Springer, Cham. https://doi.org/10.1007/978-3-030-00114-8_46
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DOI: https://doi.org/10.1007/978-3-030-00114-8_46
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