ARTICLE | doi:10.20944/preprints202207.0357.v1
Subject: Mathematics & Computer Science, Probability And Statistics Keywords: software reliability models; proportional intensity model; non-homogeneous Poisson process; time-dependent covariate; maximum likelihood estimation; goodness-of-fit performance; predictive performance
Online: 25 July 2022 (08:13:12 CEST)
This paper focuses on the so-called proportional intensity-based software reliability models (PI-SRMs), which are extensions of the common non homogeneous Poisson process (NHPP)-based SRMs, and describe the probabilistic behavior of software fault-detection process by incorporating the time-dependent software metrics data observed in the development process. Especially we generalize the seminal PI-SRM in Rinsaka, Shibata and Dohi (2006) by introducing eleven well-known fault-detection time distributions, and investigate their goodness-of-fit and predictive performances. In numerical illustrations with four data sets collected in real software development projects, we utilize the maximum likelihood estimation to estimate model parameters with three time-dependent covariates; test execution time, failure identification work and computer time-failure identification, and examine the performances of our PI SRMs in comparison with the existing NHPP-based SRMs without covariates. It is shown that our PI-STMs could give better goodness-of-fit and predictive performances in many cases.