Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Handbook of software reliability engineering
Handbook of software reliability engineering
Software reliability models: an approach to early reliability prediction
ISSRE '96 Proceedings of the The Seventh International Symposium on Software Reliability Engineering
Residual fault density prediction using regression methods
ISSRE '96 Proceedings of the The Seventh International Symposium on Software Reliability Engineering
Software Reliability Growth Modeling: Models and Applications
IEEE Transactions on Software Engineering
A time/structure based software reliability model
Annals of Software Engineering
Towards quantitative software reliability assessment in incremental development processes
Proceedings of the 33rd International Conference on Software Engineering
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The traditional approach of reliability prediction using software reliability growth models requires a large number of failures which might not be available at the beginning of the testing. The commonly used maximum likelihood estimates may not even exist or converge to a reasonable value. In this paper, an approach of making use of information from similar projects in order to obtain an early estimation of one model parameter for a current project is studied. As most of the two-parameter reliability growth models contains one parameter related to the number of faults in the software and a reliability growth rate parameter related to the testing efficiency, information from a similar project can used to estimate the reliability growth rate parameter and the limited failure data from initial testing is used to estimate the other parameter. Our case study shows that this approach is very easy to use as the estimation does not require a numerical algorithm and it always exists. It is also very stable and when the maximum likelihood estimates exist and are reasonable, our approach gives values very close to that, and the approximate confidence interval is overlapping for most cases.