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Title: Reducing the Cost of Risk-based Testing: Management of Testing Options to Manage Risk in Test and Evaluation
Authors: Karl D. Pfeiffer
Valery A. Kanevsky
Thomas J. Housel
Keywords: Diagnostic Testing
Regression Testing
Automated Testing
Monte Carlo Simulation
Sequential Bayesian Inference
Issue Date: 1-Jul-2009
Publisher: Acquisition Research Program
Citation: Published--Unlimited Distribution
Series/Report no.: Risk Analysis
Abstract: In 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 money. 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.
Description: Acquisition Management / NPS Faculty Research
Appears in Collections:Sponsored Acquisition Research & Technical Reports

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