Statistical Inference for General-Order-Statistics and Nonhomogeneous-Poisson-Process Software Reliability Models

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. >

[1]  Z. Jelinski,et al.  Software reliability Research , 1972, Statistical Computer Performance Evaluation.

[2]  A. Raftery.  Inference and Prediction for a General Order Statistic Model with Unknown Population Size. , 1987 .

[3]  Nozer D. Singpurwalla,et al.  A Unification of Some Software Reliability Models , 1985 .

[4]  Shunji Osaki,et al.  Software Reliability Growth Modeling: Models and Applications , 1985, IEEE Transactions on Software Engineering.

[5]  N. Reid,et al.  Estimating the Number of Faults in a System , 1985 .

[6]  Sheldon M. Ross,et al.  Statistical Estimation of Software Reliability , 1985, IEEE Transactions on Software Engineering.

[7]  Jerald F. Lawless,et al.  Statistical Models and Methods for Lifetime Data. , 1983 .

[8]  Bev Littlewood,et al.  Evaluation of competing software reliability predictions , 1986, IEEE Transactions on Software Engineering.

[9]  Wayne Nelson,et al.  Applied life data analysis , 1983 .

[10]  Nozer D. Singpurwalla,et al.  An Empirical Stopping Rule for Debugging and Testing Computer Software , 1977 .

[11]  K Okumoto,et al.  TIME-DEPENDENT ERROR-DETECTION RATE MODEL FOR SOFTWARE AND OTHER PERFORMANCE MEASURES , 1979 .

[12]  Amrit L. Goel,et al.  Time-Dependent Error-Detection Rate Model for Software Reliability and Other Performance Measures , 1979, IEEE Transactions on Reliability.

[13]  J. G. Kalbfleisch.  Probability and Statistical Inference , 1977 .

[14]  Edward N. Adams,et al.  Optimizing Preventive Service of Software Products , 1984, IBM J. Res. Dev..

[15]  Bev Littlewood,et al.  Stochastic Reliability-Growth: A Model for Fault-Removal in Computer-Programs and Hardware-Designs , 1981, IEEE Transactions on Reliability.

[16]  Douglas R. Miller.  Exponential order statistic models of software reliability growth , 1986, IEEE Transactions on Software Engineering.

[17]  K. Roeder,et al.  A Unified Treatment of Integer Parameter Models , 1987 .

[18]  P. J. Green,et al.  Probability and Statistical Inference , 1978 .

[19]  N. Reid,et al.  On the Software Reliability Models of Jelinski-Moranda and Littlewood , 1985, IEEE Transactions on Reliability.

[20]  John D. Kalbfleisch,et al.  Application of Likelihood Methods to Models Involving Large Numbers of Parameters , 1970 .