• Convergence Security Journal
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• v.6 no.3
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• pp.117-126
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• 2006

Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, could avoid multiple integration using Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference for general order statistics models in software reliability with diffuse prior information and model selection method are studied. For model determination and selection, explored goodness of fit (the error sum of squares), trend tests. The methodology developed in this paper is exemplified with a software reliability random data set introduced by of Weibull distribution(shape 2 & scale 5) of Minitab (version 14) statistical package.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.733-735
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• 2017

Because of the increasing necessity on sharing economy, numerous number of commercial platform based on sharing has been on the rise: car sharing, bike sharing, house sharing, book sharing, etc. And most of all, share house which aimed at dwelling problem, showed the greatest growth than the other economic models. But during the growth period, unforeseen problems also revealed its traces; the vacancy and reliability problem on the mutual platform. However, there is no way to resolve this situation now. Due to this reason, we will offer some keys to unlock existing problems on current share house platform here.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.2
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• pp.33-39
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• 2002

Accelerated life testing of a car is used to get information quickly on its life distribution. Test cars are no under severe conditions and fail sooner than under usual conditions. A model is fitted to the accelerated failure times and then extrapolated to estimate the life distribution under usual conditions. This paper presents an accelerated test md the reliability growth theory, and applies it to some subsystems of cars during their prototype and pilot testing. The data presented illustrates explicitly the prediction of the reliability growth in the product development cycle. The application of these techniques is a part of the product assurance function that plays an important role in product reliability improvement.

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.231-241
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• 1996

The reliability in software has expand in quality and quantity, also its importance and role are increased. But, a study of software reliability is lack of development. this paper software reliability growth models(SRGM) described by NonHome-geneous Poisson(NHPP)processes. Using actual software error data observed by software testing the SRGM's are composition of error-class, and error-class by three class. this paper made the reliability-model of software using three error- class. The purpose of this study to increase software productivity and to improve software quality. So to achive these goals we focused a study of software reliability model using the error-class.

• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2345-2352
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• 1998

The hyper-geometric distribution software reliability growth model was recently developed and successfully applied Due to mathematical difficultv of the maximum likclihmd method, the least squares method has hem suggested for parameter estimation by the previous studies. We first summarize and compare the minimization criteria adopted by the previous studies. It is theo shown that the weighted least squares method is more appropriate hecause of the nonhomogeneous variability of the number of newly detected faults. The adequacy of the weighted least squares method is illustrated by two numerical examples. Finally, we propose a new method fur predicting the number of faults newly discovered by next test instances. The new prediction method can be used for determining the time to stop testing.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3490-3499
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• 1999

The hyper-geometric distribution software reliability growth model (HGDM) was recently developed and successfully applied to the problem of estimating the number of initial faults residual in a software at the beginning of the test-and-debug phase. Though the HGDM is a time-domain software reliability growth model(SRGM), it is not possible to compare the HGDM with other time-domain SRGMs. Furthermore the usual software reliability can not be computed. These drawbacks are derived from fact that the HGDM is not described in terms of the execution time. Thus we develop a continuous-time HGDM with binomial sensitivity factor in order to remove these drawbacks. Statistical characteristics of the suggested model are studied and its applicability is then examined by analyzing real test data sets. It is empirically shown that the continuous-time HGDM with binomial sensitivity factor can be used as an alternative to the current HGDM.

• Convergence Security Journal
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• v.12 no.1
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• pp.9-16
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• 2012

Due to the large scale application of software systems, software reliability plays an important role in software developments. In this paper, a software reliability growth model (SRGM) is proposed for testing time. The testing time on the right is truncated in this model. The intensity function, mean-value function, reliability of the software, estimation of parameters and the special applications of Pareto NHPP model are discussed. This paper, a numerical example of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection, depended on difference between predictions and actual values, were efficient using the mean square error and $R_{SQ}$.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.755-762
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• 2013

In this paper we consider the NHPP software reliability model. And we deal with the maximum likelihood estimation and the Bayesian estimation with conjugate prior for parameter inference in the mean value function of Goel-Okumoto model (1979). The parameter estimates for the proposed model is presented by MLE and Bayes estimator in data set. We compare the predicted number of faults with the actual data set using the proposed mean value function.

• Journal of the Microelectronics and Packaging Society
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• v.15 no.4
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• pp.17-24
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• 2008

Thermal annealing and electromigration test were performed at $150^{\circ}C$ and $150^{\circ}C,\;5{\times}10^4\;A/cm^2$ conditions, respectively, in order to compare the growth kinetics of intermetallic compound(IMC) in Cu pillar bump. The quantitative interfacial adhesion energy with annealing was measured by using four-point bending strength test in order to assess the effect of IMC growth on the mechanical reliability of Cu pillar bump. Only $Cu_6Sn_5$ was observed in the Cu pillar/Sn interface after reflow. However, $Cu_3Sn$ formed and grew at Cu pillar/$Cu_6Sn_5$ interface with increasing annealing and stressing time. The growth kinetics of total($Cu_6Sn_5+Cu_3Sn$) IMC changed when all Sn phases in Cu pillar bump were exhausted. The complete consumption time of Sn phase in electromigration condition was faster than that in annealing condition. The quantitative interfacial adhesion energy after 24h at $180^{\circ}C$ was $0.28J/m^2$ while it was $3.37J/m^2$ before annealing. Therefore, the growth of IMC seem to strongly affect the mechanical reliability of Cu pillar bump.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.2
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• pp.473-480
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• 1996

Automatic fatigue crack length measuring system using DC electrical potential method and the system control program for automatic fatigue testing under random load condition were made in this study. And using these system and control program, fatigue tests were executed under constant and random load condition. As the result, the propagation of crack in random loading can be represented Paris equaiton and log normal probability function. But constant and random load test show different crack propagation properties.