Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/2665
Title: | Making Big Data, Safe Data: A Test Optimization Approach |
Authors: | Ricardo Valerdi |
Keywords: | Big Data Contractor Performance |
Issue Date: | 15-Jun-2016 |
Publisher: | Acquisition Research Program |
Citation: | Published--Unlimited Distribution |
Series/Report no.: | Test and Evaluation (T&E) UOA-TE-16-148 |
Abstract: | This 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. |
Description: | Test and Evaluation (T&E) / Grant-funded Research |
URI: | https://dair.nps.edu/handle/123456789/2665 |
Appears in Collections: | Sponsored Acquisition Research & Technical Reports |
Files in This Item:
File | Size | Format | |
---|---|---|---|
UOA-TE-16-148.pdf | 3.43 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.