• IE interfaces
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• v.19 no.1
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• pp.19-25
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• 2006

In the development process, the objective of a reliability growth program is to track the increase in system reliability, and determine as early as possible whether or not the system reliability is growing at a sufficient rate to meet the required goal and allocate available resources accordingly. Implementation of this kind of program will provide very useful information on concept selection, product/process reliability, and cost effectiveness without too much time, money and engineering effort being spent on the development of failure suspect parts. The purpose of this research is to present a practical method for efficiently monitoring a reliability growth test process using AMSAA(Army Materiel Systems Analysis Activity) reliability growth model. The presented growth management is a viable method for identifying failure modes, incorporating design changes and monitoring reliability progress on an on-going basis during the early stages of a product development program.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.10
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• pp.747-752
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• 2019

By using the failure information and the cumulative test execution time obtained by performing the reliability growth test, it is possible to estimate the parameter of the reliability growth model, and the Mean Time Between Failure (MTBF) of the product can be predicted through the parameter estimation. However the failure information could be acquired periodically or the number of sample data of the obtained failure information could be small. Because there are various constraints such as the cost and time of test or the characteristics of the product. This may cause the error of the parameter estimation of the reliability growth model to increase. In this study, the Bayesian method is applied to estimating the parameters of the reliability growth model when the number of sample data for the fault information is small. Simulation results show that the estimation accuracy of Bayesian method is more accurate than that of Maximum Likelihood Estimation (MLE) respectively in estimation the parameters of the reliability growth model.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.1
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• pp.1-8
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• 2020

In this paper we proposed to develop the reliability growth planning for the One-shot system using the PM2-Discrete model. The PM2-Discrete is the methodology specifically developed for discrete systems and is the first quantitative method available for formulating detailed plans in the discrete usage domain. First, the parameters RG, RI, T, MS and d of the PM2-Discrete model are set. Second, the case analysis was performed on One-shot system A. Third, the input parameter values were applied to drive the R(t) equation. Finally, using RGA 11 Software, the reliability Growth Planning Curve of One-shot system A was constructed. Also, the sensitivity analyses are performed for the changes of model parameters. The results of this study can be usefully used in establishing the reliability growth planning curve of the One-shot system.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.110-121
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• 2004

There is a new trend of incorporating software coverage metrics into software reliability modelling. This paper proposes a coverage-based software reliability growth model. Firstly, the failure rate function in coverage is analytically derived. Then it is shown that the number of detected faults follows a Nonhomogeneous Poisson distribution of which intensity function is the failure rate function in coverage. Practical applicability of the proposed models is examined by illustrative numerical examples.

• Journal of Korean Society for Quality Management
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• v.34 no.4
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• pp.133-138
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• 2006

In this paper, we propose a software reliability growth model (SRGM) based on the testing domain, which is isolated by the executed test cases. This model assumes an imperfect debugging environment in which new faults are introduced in the fault-correction process. We consider that the fault detection rate of NHPP model is changed in the proposed SRGM. We obtain the maximum likelihood estimate, and compare goodness-of-fit with another existing software reliability growth model.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1451-1458
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• 2004

Current software reliability growth models based on Gompertz growth curve are all logarithmic type. Software reliability growth models based on logarithmic type Gompertz growth curve has difficulties in parameter estimation. Therefore this paper proposes a software reliability growth model based on the logistic type Gompertz growth curie. Its usefulness is empirically verified by analyzing the failure data sets obtained from 13 different software projects. The parameters of model are estimated by linear regression through variable transformation or Virene's method. The proposed model is compared with respect to the average relative prediction error criterion. Experimental results show that the pro-posed model performs better the models based on the logarithmic type Gompertz growth curve.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.815-823
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• 2012

In this paper reliability growth model in which the operating time between successive failure is a continuous random variable is proposed. This model is for Burr type XII distribution with two parameters which is discussed in two versions: the order statistics and non-homogeneous Poisson process. The two software reliability measures are obtained. The performance for two versions of the suggested model is tested on real data set by U-plot and Y-plot using Kolmogorov distance.

• Journal of Applied Reliability
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• v.5 no.3
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• pp.381-391
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• 2005

The level of reliability attained largely depends upon the investment in reliability growth programs during development phase. In order to find the relationship between reliability growth test time and BRTE(basic reliability tasks effectiveness) in a reliability improvement program that minimizes LCC in which contains the reliability growth cost, repair and replacement costs, and spare parts ordering costs in service with given service rate in management policy, the growth rate has been suggested proper LCC versus growth rate. This model employs the reliability growth projection with delayed fixes in avionic equipment based on AMSAA.

• Journal of Korean Society for Quality Management
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• v.36 no.1
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• pp.89-95
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• 2008

In this paper, we propose a software reliability growth model based on the superposition cause in the software system, which is isolated by the executed test cases in software testing. In particular, our model assumes an imperfect debugging environment in which new faults are introduced in the fault-correction process, and is formulated as a nonhomogeneous Poisson process(NHPP). Further, it is applied to fault-detection data, the results of software reliability assessment are shown, and comparison of goodness-of-fit with the existing software reliability growth model is performed.

• Journal of Korean Society for Quality Management
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• v.26 no.3
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• pp.130-140
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• 1998

Studies present a guide to parameter estimation of software reliability models using SAS JMP. In this paper, we consider only software reliability growth model(SRGM), where mean value function has a S-shaped growth curve, such as Yamada et al. model, and ohba inflection model. Besides these stochastic SRGM, deterministic SRGM's, by fitting Logistic and Gompertz growth curve, have been widely used to estimate the error content of software systems. Introductions or guide lines of JMP are concerned. Estimation of parameters of Yamada et al. model and Logistic model is accomplished by using JMP. The differences between Yamada et al. model and Logistic model is accomplished by using JMP. The differences between Yamada et al. model and Logistic model is discussed, along with the variability in the estimates or error sum of squares. This paper have shown that JMP can be an effective tool I these research.