A Statistical Estimation Method of Optimal Software Release Timing Applying Auto-Regressive Models
Summary :
This paper proposes a statistical method to estimate the optimal software release time which minimizes the expected total software cost incurred in both testing and operation phases. It is shown that the underlying cost minimization problem can be reduced to a graphical one. This implies that the software release problem under consideration is essentially equivalent to a time series forecasting for the software fault-occurrence time data. In order to predict the future fault-occurrence time, we apply three extraordinary auto-regressive models by Singpurwalla and Soyer (1985) as the prediction devices as well as the well-known AR and ARIMA models. Numerical examples are devoted to illustrate the predictive performance for the proposed method. We compare it with the classical exponential software reliability growth model based on the non-homogeneous Poisson process, using actual software fault-occurrence time data.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.1 pp.331-338
- Publication Date
- 2001/01/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Reliability, Maintainability and Safety Analysis
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Tadashi DOHI, Hiromichi MORISHITA, Shunji OSAKI, "A Statistical Estimation Method of Optimal Software Release Timing Applying Auto-Regressive Models" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 1, pp. 331-338, January 2001, doi: .
Abstract: This paper proposes a statistical method to estimate the optimal software release time which minimizes the expected total software cost incurred in both testing and operation phases. It is shown that the underlying cost minimization problem can be reduced to a graphical one. This implies that the software release problem under consideration is essentially equivalent to a time series forecasting for the software fault-occurrence time data. In order to predict the future fault-occurrence time, we apply three extraordinary auto-regressive models by Singpurwalla and Soyer (1985) as the prediction devices as well as the well-known AR and ARIMA models. Numerical examples are devoted to illustrate the predictive performance for the proposed method. We compare it with the classical exponential software reliability growth model based on the non-homogeneous Poisson process, using actual software fault-occurrence time data.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_1_331/_p
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@ARTICLE{e84-a_1_331,
author={Tadashi DOHI, Hiromichi MORISHITA, Shunji OSAKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Statistical Estimation Method of Optimal Software Release Timing Applying Auto-Regressive Models},
year={2001},
volume={E84-A},
number={1},
pages={331-338},
abstract={This paper proposes a statistical method to estimate the optimal software release time which minimizes the expected total software cost incurred in both testing and operation phases. It is shown that the underlying cost minimization problem can be reduced to a graphical one. This implies that the software release problem under consideration is essentially equivalent to a time series forecasting for the software fault-occurrence time data. In order to predict the future fault-occurrence time, we apply three extraordinary auto-regressive models by Singpurwalla and Soyer (1985) as the prediction devices as well as the well-known AR and ARIMA models. Numerical examples are devoted to illustrate the predictive performance for the proposed method. We compare it with the classical exponential software reliability growth model based on the non-homogeneous Poisson process, using actual software fault-occurrence time data.},
keywords={},
doi={},
ISSN={},
month={January},}
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TY - JOUR
TI - A Statistical Estimation Method of Optimal Software Release Timing Applying Auto-Regressive Models
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 331
EP - 338
AU - Tadashi DOHI
AU - Hiromichi MORISHITA
AU - Shunji OSAKI
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2001
AB - This paper proposes a statistical method to estimate the optimal software release time which minimizes the expected total software cost incurred in both testing and operation phases. It is shown that the underlying cost minimization problem can be reduced to a graphical one. This implies that the software release problem under consideration is essentially equivalent to a time series forecasting for the software fault-occurrence time data. In order to predict the future fault-occurrence time, we apply three extraordinary auto-regressive models by Singpurwalla and Soyer (1985) as the prediction devices as well as the well-known AR and ARIMA models. Numerical examples are devoted to illustrate the predictive performance for the proposed method. We compare it with the classical exponential software reliability growth model based on the non-homogeneous Poisson process, using actual software fault-occurrence time data.
ER -
English
