Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Comprehensive Analysis of Proportional Intensity-based Software Reliability Models with Covariates

Version 1 : Received: 22 July 2022 / Approved: 25 July 2022 / Online: 25 July 2022 (08:13:12 CEST)

How to cite: Li, S.; Dohi, T.; Okamura, H. A Comprehensive Analysis of Proportional Intensity-based Software Reliability Models with Covariates. Preprints 2022, 2022070357. https://doi.org/10.20944/preprints202207.0357.v1 Li, S.; Dohi, T.; Okamura, H. A Comprehensive Analysis of Proportional Intensity-based Software Reliability Models with Covariates. Preprints 2022, 2022070357. https://doi.org/10.20944/preprints202207.0357.v1

Abstract

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.

Keywords

software reliability models; proportional intensity model; non-homogeneous Poisson process; time-dependent covariate; maximum likelihood estimation; goodness-of-fit performance; predictive performance

Subject

Computer Science and Mathematics, Probability and Statistics

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