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dc.contributor.authorPfeiffer, Karl D.
dc.contributor.authorKanevsky, Valery A.
dc.contributor.authorHousel, Thomas J.
dc.date2009
dc.date.accessioned2012-03-14T17:01:35Z
dc.date.available2012-03-14T17:01:35Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/10945/635
dc.description.abstractIn the acquisition or management of complex systems, testing is the means by which we trade budget or schedule for information about the likelihood our system will work correctly under operational load. Branch paths in hardware and software increase as a function of the number of components and interconnections, leading to exponential growth in the number of test cases required for exhaustive examination, or perfect knowledge, of a complex system. In practice, the typical cost for testing in schedule or in budget means that only a small fraction of these paths are investigated. In this work, we develop an abstract model to describe system testing and the information return (or reduction in risk) for the attendant cost in time and mondey. This model is supported by a mathematical analysis suitable for Monte Carlo simulation. The long-term goal of this modeling work is to construct a decision-support tool for the Navy Program Executive Office Integrated Warfare Systems (PEO IWS) offering quantitative information about cost versus diagnostic certainty in system testing.en_US
dc.format.extentxii, 63 p.: ill.;28 cm.en_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsApproved for public release, distribution unlimiteden_US
dc.titleReducing the cost of risk-based testing: management of testing options to manage risk in test and evaluation / by Karl D. Pfeiffer, Valery A. Kanevsky, Thomas J. Housel.en_US
dc.typeReporten_US
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.schoolGraduate School of Business & Public Policy (GSBPP)
dc.contributor.departmentGraduate School of Business & Public Policy (GSBPP)
dc.subject.authorMonte Carlo methoden_US
dc.identifier.oclcocn671240528
dc.identifier.npsreportNPS-GSBPP-09-027


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