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dc.contributor.authorValerdi, Ricardo
dc.date.accessioned2017-05-30T18:23:25Z
dc.date.available2017-05-30T18:23:25Z
dc.date.issued2016-06-01
dc.identifier.urihttp://hdl.handle.net/10945/53455
dc.description.abstractThis report outlines a procedure and algorithm to optimize the potential knowledge gained about a complex system when performing robustness testing and faced with a set of constraints. In particular, this project was catalyzed by the need to put a value on testing. Included with this project report is a proof of concept created in MS Excel utilizing its VBA developer tool. In short, a test network is created by establishing test relationships and then assigning each an expected knowledge value. With these values and an understanding about the relationships between the tests, an optimization about the total potential knowledge of the system can b e acquired while minimizing testing costs and/or effort.en_US
dc.description.sponsorshipNaval Postgraduate School Acquisition Research Programen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleMaking Big Data, Safe Data: A Test Optimization Approachen_US
dc.typeReporten_US
dc.identifier.npsreportUOA-TE-16-148en_US


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