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Statistical Inference for General-Order-Statistics and Nonhomogeneous-Poisson-Process Software Reliability Models
November 1989 (vol. 15 no. 11)
pp. 1485-1490

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.

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Index Terms:
statistical inference; general-order-statistics; nonhomogeneous-Poisson-process software reliability models; debugging; asymptotic likelihood inference; asymptotic confidence levels; interval estimates; conditional failure rate; inference mechanisms; software reliability; statistical analysis
Citation:
H. Joe, "Statistical Inference for General-Order-Statistics and Nonhomogeneous-Poisson-Process Software Reliability Models," IEEE Transactions on Software Engineering, vol. 15, no. 11, pp. 1485-1490, Nov. 1989, doi:10.1109/32.41340
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