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Making Big Data, Safe Data: A Test Optimization Approach

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University of Arizona Tucson United States

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The following 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 be acquired while minimizing testing costs andor effort.

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Technical Report



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Approved For Public Release;

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