• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.569-571
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• 1999

소프트웨어의 신뢰도는 하드웨어의 신뢰도와 고장메타니즘이 다르므로 하드웨어의 신뢰도 모델을 그대로 이용할 수 없다. 소프트웨어의 신뢰도를 추정하기 위한 방법은 그동안 Jelinski-Moranda(JM) 모델을 비롯하여 많은 기법이 연구되었다. 그러나, 아직까지 만족하다고 인정할 만한 신뢰도모델링은 개발되지 않았다. 본 연구에서는 소프트웨어의 테스트를 통하여 검출되는 에러 개수의 추세를 가지고 비제차포아송과정(NHPP)의 파라미터를 찾아 신뢰도함수를 구하고자 하며, 아울러, 테스트중단시간을 결정하고자 한다. 파라미터를 찾는 방법은 maximum likelihood estimate(MLE) 기법을 이용하며, 테스트 중단시간은 구해진 파라미터를 신뢰도 함수에 대입하여 결정한다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.49
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• pp.23-31
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• 1999

Bayesian approach using nonhomogeneous Poisson process is considered for modelling software reliability problem. The usefulness of the iterative sampling-based method increases greatly as the dimension of a problem increases. Maximum likelihood estimator and Gibbs estimator are derived. Model selection based on a predictive likelihood is studied. A numerical example is given.

• Journal of IKEEE
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• v.15 no.1
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• pp.43-48
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• 2011

Decision problem called an optimal release policies, after testing a software system in development phase and transfer it to the user, is studied. The infinite failure non-homogeneous Poisson process models presented and propose an optimal release policies of the life distribution applied extreme distribution which used to find the minimum (or the maximum) of a number of samples of various distributions. In this paper, discuss optimal software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement. In a numerical example, extreme value distribution as another alternative of existing the Poisson execution time model and the log power model can be verified using inter-failure time data.

• Tunnel and Underground Space
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• v.8 no.3
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• pp.249-256
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• 1998

Pocheon, Keochang and Sangju granite samples of different granularity and mineralogical composition were thermally treated at pre-determined temperature of $600^{\circ}C$. Thermally-induced microcracks were characterized using an optical microscopy and their effects on the deformation behavior of thermally cycled samples were studied performing compressive mechanical tests. Optical observations shows that by $600^{\circ}C$ nearlly all crystal boundaries open and the new intracrystalline cracks form in the more grains. The intracrystalline cracks are most pronounced at thermally treated Pocheon and Keochang granite samples. Results from mechanical tests represents negative lateral strains, which give negative Poisson's ratios. It is the most probable that negative lateral strains are produced by residual stresses induced during cooling.

• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.395-400
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• 1997

An important quality characteristic of a software reliability.Software reliablilty growh models prvied the tools to evluate and moniter the reliabolty growth behavior of the sofwate during the testing phase Therefore failure data collected during the testing phase should be continmuosly analyzed on the basis of some selected software reliability growth models.For the cases where nonhomogeneous Poisson proxess models are the candiate models,we suggest Poisson regression model, which expresses the relationship between the expeted and actual failures counts in disjonint time intervals,for analyzing the failure count data.The weighted lest squares method is then used to-estimate the paramethers in the parameters in the model:The resulting estimators are equivalent to the maximum likelihood estimators. The method is illustrated by analyzing the failutr count data gathered from a large- scale switchong system.

• Convergence Security Journal
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• v.10 no.1
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• pp.55-61
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• 2010

It is of great practical interest to deciding when to stop testing a software system in development phase and transfer it to the user. This decision problem called an optimal release policies. In this paper, because of the possibility of introducing new faults when correcting or modifying the software, we were researched release comparative policies which based on infinite failure NHPP model and types of interval failure times. The policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement can optimal software release times. In a numerical example, applied data which were patterns, if intensity function constant or increasing, decreasing, estimated software optimal release time.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.119-133
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• 1999

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.7-14
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• 2006

The finite failure NHPP models proposed in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, we propose the Erlang reliability model, which can capture the increasing nature of the failure occurrence rate per fault. Equations to estimate the parameters of the Erlang finite failure NHPP model based on failure data collected in the form of inter-failure times are developed. For the sake of proposing shape parameter of the Erlang distribution, we used to the goodness-of-fit test of distribution. Data set, where the underlying failure process could not be adequately described by the existing models, which motivated the development of the Erlang model. Analysis of the failure data set which led us to the Erlang model, using arithmetic and Laplace trend tests, goodness-of-fit test, bias tests is presented.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.319-326
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• 2013

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on infinite failure model and non-homogeneous Poisson Processes (NHPP). Statistical process control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, we proposed a control mechanism based on NHPP using mean value function of logarithmic hazard learning effects property.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.27-36
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• 2005

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates Per fault. This Paper Proposes reliability model using the generalized gamma distribution, which can capture the monotonic increasing(or monotonic decreasing) nature of the failure occurrence rate per fault. Equations to estimate the parameters of the generalized gamma finite failure NHPP model based on failure data collected in the form of interfailure times are developed. For the sake of proposing shape parameter of the generalized gamma distribution, used to the special pattern. Data set, where the underlying failure process could not be adequately described by the knowing models, which motivated the development of the gamma or Weibull model. Analysis of failure data set for the generalized gamma modell, using arithmetic and Laplace trend tests . goodness-of-fit test, bias tests is presented.