Title :
Statistical inference for general-order-statistics and nonhomogeneous-Poisson-process software reliability models
Author_Institution :
Univ. Coll., London, UK
fDate :
11/1/1989 12:00:00 AM
Abstract :
There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on order statistics or nonhomogeneous Poisson processes, with asymptotic confidence levels for interval estimates of parameters. In particular, interval estimates from these models are obtained for the conditional failure rate of the software, given the data from the debugging process. The data can be grouped or ungrouped. For someone making a decision about when to market software, the conditional failure rate is an important parameter. The use of interval estimates is demonstrated for two data sets that have appeared in the literature
Keywords :
inference mechanisms; software reliability; statistical analysis; asymptotic confidence levels; asymptotic likelihood inference; conditional failure rate; debugging; general-order-statistics; interval estimates; nonhomogeneous-Poisson-process software reliability models; statistical inference; Art; Fault tolerance; Large-scale systems; Maximum likelihood estimation; Notice of Violation; Parameter estimation; Programming; Software debugging; Software reliability; Statistics;
Journal_Title :
Software Engineering, IEEE Transactions on