Goodness-of-fit techniques
Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
A logarithmic poisson execution time model for software reliability measurement
ICSE '84 Proceedings of the 7th international conference on Software engineering
High quality behavioral verification using statistical stopping criteria
Proceedings of the conference on Design, automation and test in Europe
ADVIS '00 Proceedings of the First International Conference on Advances in Information Systems
Stopping Criteria Comparison: Towards High Quality Behavioral Verification
ISQED '01 Proceedings of the 2nd International Symposium on Quality Electronic Design
Achieving the Quality of Verification for Behavioral Models with Minimum Effort
ISQED '00 Proceedings of the 1st International Symposium on Quality of Electronic Design
Failure Correlation in Software Reliability Models
ISSRE '99 Proceedings of the 10th International Symposium on Software Reliability Engineering
High Assurance Software Testing In Business And DOD
Journal of Integrated Design & Process Science
Study of the effects of MBUs on the reliability of a 150 nm SRAM device
Proceedings of the 45th annual Design Automation Conference
Estimating the probability of failure when software runs are dependent: an empirical study
ISSRE'09 Proceedings of the 20th IEEE international conference on software reliability engineering
Minimizing soft errors in TCAM devices: a probabilistic approach to determining scrubbing intervals
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Computers and Industrial Engineering
| Hi-index | 0.00 |
The probability density estimation of the number of software failures in the event of clustering or clumping of the software failures is considered. A discrete compound Poisson (CP) prediction model is proposed for the random variable X/sub rem/, which is the remaining number of software failures. The compounding distributions, which are assumed to govern the failure sizes at Poisson arrivals, are respectively taken to be geometric when failures are forgetful and logarithmic-series when failures are contagious. The expected value ( mu ) of X/sub rem/ is calculated as a function of the time-dependent Poisson and compounding distribution based on the failures experienced. Also, the variance/mean parameter for the remaining number of failures, q/sub rem/, is best estimated by q/sub past/ from the failures already experienced. Then, one obtains the PDF of the remaining number of failures estimated by CP( mu ,q). CP is found to be superior to Poisson where clumping of failures exists. Its predictive validity is comparable to the Musa-Okumoto log-Poisson model in certain cases.