IEICE TRANSACTIONS on Fundamentals
Software Reliability Assessment via Non-Parametric Maximum Likelihood Estimation
Summary :
In this paper we consider two non-parametric estimation methods for software reliability assessment without specifying the fault-detection time distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. The resulting data-driven methodologies can give the useful probabilistic information on the software reliability assessment under the incomplete knowledge on fault-detection time distribution. Throughout examples with real software fault data, it is shown that the proposed methods provide more accurate estimation results than the common parametric approach.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E98-A No.10 pp.2042-2050
- Publication Date
- 2015/10/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E98.A.2042
- Type of Manuscript
- Special Section PAPER (Special Section on Recent Developments on Reliability, Maintainability and Dependability)
- Category
Authors
Yasuhiro SAITO
Hiroshima University
Tadashi DOHI
Hiroshima University
Keyword
Latest Issue
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Yasuhiro SAITO, Tadashi DOHI, "Software Reliability Assessment via Non-Parametric Maximum Likelihood Estimation" in IEICE TRANSACTIONS on Fundamentals,
vol. E98-A, no. 10, pp. 2042-2050, October 2015, doi: 10.1587/transfun.E98.A.2042.
Abstract: In this paper we consider two non-parametric estimation methods for software reliability assessment without specifying the fault-detection time distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. The resulting data-driven methodologies can give the useful probabilistic information on the software reliability assessment under the incomplete knowledge on fault-detection time distribution. Throughout examples with real software fault data, it is shown that the proposed methods provide more accurate estimation results than the common parametric approach.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E98.A.2042/_p
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@ARTICLE{e98-a_10_2042,
author={Yasuhiro SAITO, Tadashi DOHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Software Reliability Assessment via Non-Parametric Maximum Likelihood Estimation},
year={2015},
volume={E98-A},
number={10},
pages={2042-2050},
abstract={In this paper we consider two non-parametric estimation methods for software reliability assessment without specifying the fault-detection time distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. The resulting data-driven methodologies can give the useful probabilistic information on the software reliability assessment under the incomplete knowledge on fault-detection time distribution. Throughout examples with real software fault data, it is shown that the proposed methods provide more accurate estimation results than the common parametric approach.},
keywords={},
doi={10.1587/transfun.E98.A.2042},
ISSN={1745-1337},
month={October},}
Copy
TY - JOUR
TI - Software Reliability Assessment via Non-Parametric Maximum Likelihood Estimation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2042
EP - 2050
AU - Yasuhiro SAITO
AU - Tadashi DOHI
PY - 2015
DO - 10.1587/transfun.E98.A.2042
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E98-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2015
AB - In this paper we consider two non-parametric estimation methods for software reliability assessment without specifying the fault-detection time distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. The resulting data-driven methodologies can give the useful probabilistic information on the software reliability assessment under the incomplete knowledge on fault-detection time distribution. Throughout examples with real software fault data, it is shown that the proposed methods provide more accurate estimation results than the common parametric approach.
ER -
English
