Software Reliability Measurement and Assessment with Stochastic Differential Equations
Summary :
In this paper, we propose a plausible software reliability growth model by applying a mathematical technique of stochastic differential equations. First, we extend a basic differential equation describing the average behavior of software fault-detection processes during the testing phase to a stochastic differential equation of ItÔ type, and derive a probability distribution of its solution processes. Second, we obtain several software reliability measures from the probability distribution. Finally, applying a method of maximum-likelihood we estimate unknown parameters in our model by using available data in the actual software testing procedures, and numerically show the stochastic behavior of the number of faults remaining in the software system. Further, the model is compared among the existing software reliability growth models in terms of goodness-of-fit.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E77-A No.1 pp.109-116
- Publication Date
- 1994/01/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Reliability)
- Category
- Software Reliability
Authors
Keyword
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Shigeru YAMADA, Mitsuhiro KIMURA, Hiroaki TANAKA, Shunji OSAKI, "Software Reliability Measurement and Assessment with Stochastic Differential Equations" in IEICE TRANSACTIONS on Fundamentals,
vol. E77-A, no. 1, pp. 109-116, January 1994, doi: .
Abstract: In this paper, we propose a plausible software reliability growth model by applying a mathematical technique of stochastic differential equations. First, we extend a basic differential equation describing the average behavior of software fault-detection processes during the testing phase to a stochastic differential equation of ItÔ type, and derive a probability distribution of its solution processes. Second, we obtain several software reliability measures from the probability distribution. Finally, applying a method of maximum-likelihood we estimate unknown parameters in our model by using available data in the actual software testing procedures, and numerically show the stochastic behavior of the number of faults remaining in the software system. Further, the model is compared among the existing software reliability growth models in terms of goodness-of-fit.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e77-a_1_109/_p
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@ARTICLE{e77-a_1_109,
author={Shigeru YAMADA, Mitsuhiro KIMURA, Hiroaki TANAKA, Shunji OSAKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Software Reliability Measurement and Assessment with Stochastic Differential Equations},
year={1994},
volume={E77-A},
number={1},
pages={109-116},
abstract={In this paper, we propose a plausible software reliability growth model by applying a mathematical technique of stochastic differential equations. First, we extend a basic differential equation describing the average behavior of software fault-detection processes during the testing phase to a stochastic differential equation of ItÔ type, and derive a probability distribution of its solution processes. Second, we obtain several software reliability measures from the probability distribution. Finally, applying a method of maximum-likelihood we estimate unknown parameters in our model by using available data in the actual software testing procedures, and numerically show the stochastic behavior of the number of faults remaining in the software system. Further, the model is compared among the existing software reliability growth models in terms of goodness-of-fit.},
keywords={},
doi={},
ISSN={},
month={January},}
Copy
TY - JOUR
TI - Software Reliability Measurement and Assessment with Stochastic Differential Equations
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 109
EP - 116
AU - Shigeru YAMADA
AU - Mitsuhiro KIMURA
AU - Hiroaki TANAKA
AU - Shunji OSAKI
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E77-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 1994
AB - In this paper, we propose a plausible software reliability growth model by applying a mathematical technique of stochastic differential equations. First, we extend a basic differential equation describing the average behavior of software fault-detection processes during the testing phase to a stochastic differential equation of ItÔ type, and derive a probability distribution of its solution processes. Second, we obtain several software reliability measures from the probability distribution. Finally, applying a method of maximum-likelihood we estimate unknown parameters in our model by using available data in the actual software testing procedures, and numerically show the stochastic behavior of the number of faults remaining in the software system. Further, the model is compared among the existing software reliability growth models in terms of goodness-of-fit.
ER -
English
