Francisco J. Samaniego and Yun Sam Chong, University Of California, Davis
It is common to plan a life test based on the assumption of exponentiality of observed lifetimes or lives between failures. Analysts are then able to calculate specifically how many items should be placed on test (or the number of observed failures it takes to terminate the test) and the maximum total time on test required to resolve the hypothesis test of interest. Once the test data are in hand, one has the opportunity to confirm the exponentiality assumption or to decide that an alternative modeling assumption is preferable. This paper pursues the question: "What if the data point toward a non-exponential Weibull model?" We identify circumstances in which the available data permit testing the original hypotheses with better performance characteristics (that is, smaller error probabilities) than the test originally planned; a complementary analysis of situations leading to poorer performance is also given. We give indications of the potential savings in the number of systems and the time on test that would accrue from having modeled the experiment correctly in the first place.
Various approaches to testing hypotheses concerning Weibull means are discussed. The first two sections of the paper are expository and review the main issues in exponential life testing and some properties and procedures associated with the Weibull distribution. In Sections three and four we develop the mechanics of Weibull life testing, and carefully examine the performance of Weibull life tests based on exponential life test plans.
Jesse H. Poore, University of Tennessee, and Carmen J. Trammel, Software Engineering Technology, Inc.
Defense systems are becoming increasingly software intensive. While software enhances the effectiveness and flexibility of these systems, it also introduces vulnerabilities related to inadequacies in software design, maintenance, and configuration control. Effective testing of these systems must take into account the special vulnerabilities introduced by software. The software testing problem is complex because of the astronomical number of scenarios and states of use. The domain of testing is large and complex beyond human intuition. Because the software testing problem is so complex, statistical principles must be used to guide testing strategy in order to get the best information for the resources invested in testing.