Exponential Regression-Based Software Reliability Model and Its Computational Aspect
Summary :
An exponential regression-based model with stochastic intensity is developed to describe the software reliability growth phenomena, where the software testing metrics depend on the intensity process. For such a generalized modeling framework, the common maximum likelihood method cannot be applied any more to the parameter estimation. In this paper, we propose to use the pseudo maximum likelihood method for the parameter estimation and to seek not only the model parameters but also the software reliability measures approximately. It is shown in numerical experiments with real software fault data that the resulting software reliability models based on four parametric approximations provide the better goodness-of-fit performance than the common non-homogeneous Poisson process models without testing metric information.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E95-A No.9 pp.1461-1468
- Publication Date
- 2012/09/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E95.A.1461
- Type of Manuscript
- Special Section PAPER (Special Section on Software Reliability Engineering)
- Category
Authors
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Shinya IKEMOTO, Tadashi DOHI, "Exponential Regression-Based Software Reliability Model and Its Computational Aspect" in IEICE TRANSACTIONS on Fundamentals,
vol. E95-A, no. 9, pp. 1461-1468, September 2012, doi: 10.1587/transfun.E95.A.1461.
Abstract: An exponential regression-based model with stochastic intensity is developed to describe the software reliability growth phenomena, where the software testing metrics depend on the intensity process. For such a generalized modeling framework, the common maximum likelihood method cannot be applied any more to the parameter estimation. In this paper, we propose to use the pseudo maximum likelihood method for the parameter estimation and to seek not only the model parameters but also the software reliability measures approximately. It is shown in numerical experiments with real software fault data that the resulting software reliability models based on four parametric approximations provide the better goodness-of-fit performance than the common non-homogeneous Poisson process models without testing metric information.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E95.A.1461/_p
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@ARTICLE{e95-a_9_1461,
author={Shinya IKEMOTO, Tadashi DOHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Exponential Regression-Based Software Reliability Model and Its Computational Aspect},
year={2012},
volume={E95-A},
number={9},
pages={1461-1468},
abstract={An exponential regression-based model with stochastic intensity is developed to describe the software reliability growth phenomena, where the software testing metrics depend on the intensity process. For such a generalized modeling framework, the common maximum likelihood method cannot be applied any more to the parameter estimation. In this paper, we propose to use the pseudo maximum likelihood method for the parameter estimation and to seek not only the model parameters but also the software reliability measures approximately. It is shown in numerical experiments with real software fault data that the resulting software reliability models based on four parametric approximations provide the better goodness-of-fit performance than the common non-homogeneous Poisson process models without testing metric information.},
keywords={},
doi={10.1587/transfun.E95.A.1461},
ISSN={1745-1337},
month={September},}
Copy
TY - JOUR
TI - Exponential Regression-Based Software Reliability Model and Its Computational Aspect
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1461
EP - 1468
AU - Shinya IKEMOTO
AU - Tadashi DOHI
PY - 2012
DO - 10.1587/transfun.E95.A.1461
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E95-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2012
AB - An exponential regression-based model with stochastic intensity is developed to describe the software reliability growth phenomena, where the software testing metrics depend on the intensity process. For such a generalized modeling framework, the common maximum likelihood method cannot be applied any more to the parameter estimation. In this paper, we propose to use the pseudo maximum likelihood method for the parameter estimation and to seek not only the model parameters but also the software reliability measures approximately. It is shown in numerical experiments with real software fault data that the resulting software reliability models based on four parametric approximations provide the better goodness-of-fit performance than the common non-homogeneous Poisson process models without testing metric information.
ER -
English
