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. Preprints2022, 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
Li, S.; Dohi, T.; Okamura, H. A Comprehensive Analysis of Proportional Intensity-based Software Reliability Models with Covariates. Preprints2022, 2022070357. https://doi.org/10.20944/preprints202207.0357.v1
APA Style
Li, S., Dohi, T., & Okamura, H. (2022). A Comprehensive Analysis of Proportional Intensity-based Software Reliability Models with Covariates. Preprints. https://doi.org/10.20944/preprints202207.0357.v1
Chicago/Turabian Style
Li, S., Tadashi Dohi and Hiroyuki Okamura. 2022 "A Comprehensive Analysis of Proportional Intensity-based Software Reliability Models with Covariates" Preprints. 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.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.