A Parametric Empirical Bayes Model to Predict Software Reliability Growth | Zendy

Néstor Barraza | Zendy

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A Parametric Empirical Bayes Model to Predict Software Reliability Growth

Author(s) -

Néstor Barraza

Publication year - 2015

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2015.08.416

Subject(s) - computer science , bayes' theorem , reliability (semiconductor) , parametric statistics , software quality , reliability engineering , failure rate , poisson distribution , parametric model , interval (graph theory) , statistics , bayesian probability , software , mathematics , software development , artificial intelligence , combinatorics , power (physics) , physics , quantum mechanics , engineering , programming language

A new software reliability model based on the empirical Bayes estimate is developed. The number of failures estimated up to a given time is used in order to estimate the probability of failure appearance during the next time interval. Instead of a non homogeneous in time failure rate as it is usually used to model reliability growth, a failure rate depending non linearly on the previous number of failures is obtained from our model. The estimate is obtained from a mixed Poisson model where the mixing probability density function models the reliability growth. The model can be used either to simulate the cumulative failures curve or to estimate the time between failures. Data of a similar project can be used to estimate the parameters of a given project. Results of simulations and estimated mean time between failures comparing well with experimental data are also shown

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