Software Reliability Analysis Based on Hierarchical Dynamic Models and Bayesian Estimations using Machine Learning

Toru Kaise
2020 Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications  
A Bayesian dynamic method for analysis of software debugging process data is handled. It is addressed to predict states of software reliability. In the Bayesian analysis, hierarchical prior models are structured with the Boltzmann machine, and empirical and expert knowledge priors are supposed. These priors play roles recognized as representations for complex situations. The empirical prior based on observed data is used for the representation of uncertainty corrections. The prior of success
more » ... bability for the tests is assumed based on expert knowledge of engineers. The reliability is estimated based on the posterior mean of the failure states. The Bayesian inferences are derived based on the computational simulation methods, and the information criterion EIC is used to choose appropriate models.
doi:10.5687/sss.2020.23 fatcat:qtctnf3vsjcn7au2wrdiv4ntg4