On Bayesian Estimation for Software Reliability Assessment Based on a Discrete NHPP Model  
Author Shinji Inoue

 
Co-Author(s) Shigeru Yamada

 
Abstract We discuss an interval estimation approach of parameters and software reliability assessment measures of a discretized software reliability model, which has a consistency with an actual collecting activity of discrete software fault-count data and has been expected to conduct highly-accurate assessment of software reliability. The interval estimation must be important to conduct software reliability assessment because most of software reliability data are incomplete. Concretely speaking, we apply the Markov chain Monte Carlo (MCMC) method for conducing Bayesian interval estimations for software reliability assessment. Further, this paper shows numerical examples of our approach by using actual fault count data.

 
Keywords Software reliability assessment, Software reliability model, Interval estimation, Markov chain Monte Carlo, Discretized NHPP model
   
    Article #:  23-074
 
Proceedings of the 23rd ISSAT International Conference on Reliability and Quality in Design
August 3-5, 2017 - Chicago, Illinois, U.S.A.