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A cost model for testing unmanned and autonomous systems of systems

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dc.contributor.advisor Ricardo Valerdi. en_US
dc.contributor.author Deonandan, Indira D. (Indira Devi) en_US
dc.contributor.other Massachusetts Institute of Technology. Technology and Policy Program. en_US
dc.date.accessioned 2011-05-23T18:06:22Z
dc.date.available 2011-05-23T18:06:22Z
dc.date.copyright 2011 en_US
dc.date.issued 2011 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/63037
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; and, (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2011. en_US
dc.description Vita. Page 124 blank. Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 113-114). en_US
dc.description.abstract The evolutionary nature of Unmanned and Autonomous Systems of Systems (UASoS) acquisition needs to be matched by equally evolutionary test capabilities in the future. There is currently no standard method to determine what is required to make programs safe for deployment, nor is there the ability to make effective contingency plans should testing requirements change. Spending too much effort designing goals when causal understandings are still in flux is inefficient. As such, policy making and enforcing policies on the deployment of UASoS becomes very problematic. Testing is required especially for UASoS to identify risk, improve capabilities and minimize unpleasant surprises. It needs to be effective and focused, determining the issues and working towards ensuring the risks of the UASoS are known. It is important to have adequate feedback loops, a culture of information sharing and learning from best practices, as well as the development of metrics and/or performance indicators that adequately reflect the effectiveness of the test process. This thesis describes a model that is part of a larger Prescriptive and Adaptive Testing Framework (PATFrame), which uses knowledge acquisition to minimize risk through a decision support system. This work presents the cost and risk considerations for UASoS T&E and provides the preliminary parameters to conduct trade-off analyses for T&E. It also provides guidance on how the DoD can adopt such tools to transform the DoD T&E enterprise. The model is a combination of information collected from various normative and descriptive views of testing based on literature review, surveys, and interviews with members of the Department of Defense (DoD) T&E community A cost estimation model can have significant impacts on how the DoD currently does testing and would help maximize the use of the resources available. It is a model based method for calculating effort for test and evaluation and forms a baseline for strategic decision making in DoD acquisition programs. The intent is to predict within a certain probability that a test program can be completed within a certain budget given the assumptions used in characterizing the UASoS and the T&E process. en_US
dc.description.statementofresponsibility by Indira D. Deonandan. en_US
dc.format.extent 124 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Aeronautics and Astronautics. en_US
dc.subject Engineering Systems Division. en_US
dc.subject Technology and Policy Program. en_US
dc.title A cost model for testing unmanned and autonomous systems of systems en_US
dc.type Thesis en_US
dc.description.degree S.M.in Technology and Policy en_US
dc.description.degree S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. en_US
dc.contributor.department Massachusetts Institute of Technology. Engineering Systems Division. en_US
dc.contributor.department Massachusetts Institute of Technology. Technology and Policy Program. en_US
dc.identifier.oclc 722499997 en_US


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