A Generalized Bivariate Modeling Framework of Fault Detection and Correction Processes
Hiroyuki Okamura, Tadashi Dohi · 2017
This paper presents a generalized modeling framework of fault detection and correction processes with bivariate distributions. The presented framework includes almost all existing software reliability growth models, namely the models in which both fault detection and correction processes are described by non-homogeneous Poisson processes. In our framework, the time dependency of fault correction time corresponds to the correlation between fault detection and correction times. Moreover, we propose a new fault detection and correction process model with hyper-Erlang distributions, and develop the model parameter estimation algorithm via EM (expectation-maximization) algorithm. In numerical examples, we demonstrate the data fitting ability of hyper-Erlang model with actual fault detection and correction data of open source projects.