Evaluation of competing software reliability predictions
IEEE Transactions on Software Engineering - Special issue on reliability and safety in real-time process control
Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Recalibrating Software Reliability Models
IEEE Transactions on Software Engineering
New Ways to Get Accurate Reliability Measures
IEEE Software
Operational Profiles in Software-Reliability Engineering
IEEE Software
A logarithmic poisson execution time model for software reliability measurement
ICSE '84 Proceedings of the 7th international conference on Software engineering
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The large literature on software reliability assessment and prediction is essentially concerned with parametric models: the inter-failure time random variables are assumed to come from parametric families of distributions. Such models involve quite strong assumptions. The motivation for the present work is to relax these assumptions and - in the tradition of non-parametric statistics generally - 'allow the data to speak for themselves'. We present a new non-parametric model for reliability prediction which is based upon the use of kernel density estimators, and compare its accuracy on some real data sets with the predictions that come from several of the better conventional models. These initial results are encouraging: the new models seem to perform as well as the best of the earlier models.