Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Handbook of software reliability engineering
Handbook of software reliability engineering
A logarithmic poisson execution time model for software reliability measurement
ICSE '84 Proceedings of the 7th international conference on Software engineering
Proportional Intensity-Based Software Reliability Modeling with Time-Dependent Metrics
COMPSAC '06 Proceedings of the 30th Annual International Computer Software and Applications Conference - Volume 01
Metrics-Based Software Reliability Models Using Non-homogeneous Poisson Processes
ISSRE '06 Proceedings of the 17th International Symposium on Software Reliability Engineering
EM algorithm for discrete software reliability models: a unified parameter estimation method
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
A Multi-factor Software Reliability Model Based on Logistic Regression
ISSRE '10 Proceedings of the 2010 IEEE 21st International Symposium on Software Reliability Engineering
Towards quantitative software reliability assessment in incremental development processes
Proceedings of the 33rd International Conference on Software Engineering
IWSM-MENSURA '11 Proceedings of the 2011 Joint Conference of the 21st International Workshop on Software Measurement and the 6th International Conference on Software Process and Product Measurement
Hi-index | 0.00 |
A mixed Poisson model with stochastic intensity is developed to describe the software reliability growth phenomena. where the software testing metrics depend on the intensity process. For such a generalized modeling framework, the common maximum likelihood method cannot be applied any more to the parameter estimation. In this paper, we propose to use the pseudo maximum likelihood method for the parameter estimation and to seek not only the model parameters but also the software reliability measures approximately. It is shown in numerical experiments with real software fault data that the resulting software reliability models based on four parametric approximations provide the better goodness-of-fit performance than the common non-homogeneous Poisson process models without testing metric information.