Show simple item record

dc.contributor.authorPfeiffer, Karl D.
dc.contributor.authorKanevsky, Valery A.
dc.contributor.authorHousel, Thomas J.
dc.date01-Apr-09
dc.date.accessioned2013-05-08T21:17:47Z
dc.date.available2013-05-08T21:17:47Z
dc.date.issued2009-04-01
dc.identifier.urihttp://hdl.handle.net/10945/33373
dc.descriptionProceedings Paper (for Acquisition Research Program)en_US
dc.descriptionApproved for public release; distribution unlimited.en_US
dc.description.abstractTesting of complex systems is a fundamentally difficult task, whether locating faults (diagnostic testing) or implementing upgrades (regression testing). Branch paths through the system increase as a function of the number of components and interconnections, leading to exponential growth in the number of test cases for exhaustive examination. In practice, the typical cost for testing in schedule or in budget means that only a small fraction of these paths are investigated. Given some fixed cost, then, which tests should we execute to guarantee the greatest information returned for the effort? In this work, we develop an approach to system testing using an abstract model flexible enough to be applied to both diagnostic and regression testing, grounded in a mathematical model suitable for rigorous analysis and Monte Carlo simulation. Early results indicate that in many cases of interest, a good, though not optimal, solution to the fixed-constraint problem (how many tests for budget x?) can be approached as a simple best-next strategy (which test returns the highest information per unit cost?). The 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.en_US
dc.description.sponsorshipNaval Postgraduate School Acquisition Research Programen_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.titleMathematical Modeling for Optimal System Testing under Fixed-cost Constrainten_US
dc.typeReporten_US
dc.contributor.departmentAcquisition Management
dc.contributor.departmentNPS Faculty
dc.subject.authorModeling & Simulationen_US
dc.subject.authorDiagnostic Testing, Regression Testing, Automated Testing, Monte Carlo Simulation, Sequential Bayesian Inference, Knapsack Problemen_US
dc.identifier.npsreportNPS-AM-09-023


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record