Exponential order statistic models of software reliability growth
IEEE Transactions on Software Engineering
Evaluation of competing software reliability predictions
IEEE Transactions on Software Engineering - Special issue on reliability and safety in real-time process control
Software Quality Measurement Based on Fault-Detection Data
IEEE Transactions on Software Engineering
A time/structure based software reliability model
Annals of Software Engineering
Predictive analyses for nonhomogeneous Poisson processes with power law using Bayesian approach
Computational Statistics & Data Analysis
Nonparametric Analysis of the Order-Statistic Model in Software Reliability
IEEE Transactions on Software Engineering
Statistical inference and prediction for the Weibull process with incomplete observations
Computational Statistics & Data Analysis
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
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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.