• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.25-34
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• 2012

In this study, in the process of testing before the release of the software products designed, software testing manager in advance should be aware of the testing-information. Therefore, the effective learning effects perspective has been studied using the NHPP software. The finite failure nonhomogeneous Poisson process models presented and applied property of learning effect based on truncated time and delayed S-shaped software reliability. 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. As a result, the learning factor is greater than autonomous errors-detected factor that is generally efficient model can be confirmed. This paper, a failure data analysis was performed, using time between failures, according to the small sample and large sample sizes. The parameter estimation was carried out using maximum likelihood estimation method. Model selection was performed using the mean square error and coefficient of determination, after the data efficiency from the data through trend analysis was performed.

• Journal of the Korean Statistical Society
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• v.28 no.1
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• pp.125-133
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• 1999

We consider a FIFO single-server queueing model in which both the arrival and service processes are modulated by the amount of work in the system. The arrival process is a non-homogeneous Poisson process(NHPP) modulated by work, that is, with an intensity that depends on the work in the system. Each customer brings a job consisting of an exponentially distributed amount of work to be processed. The server processes the work at various service rates which also depend on the work in the system. Under the stability conditions obtained by Browne and Sigman(1992) we derive the exact stationary distribution of the work W(t) and the first exit probability that the work level b is exceeded before the work level a is reached, starting from x$\in$[a, b].

• Journal of Integrative Natural Science
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• v.10 no.4
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• pp.249-257
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• 2017

The main focus when developing software is to improve the reliability and stability of a software system. We are enjoying a very comfortable life thanks to modern civilization, however, comfort is not guaranteed to us. Once software systems are introduced, the software systems used in the field environments are the same as or close to those used in the development-testing environment; however, the systems may be used in many different locations. Development of software system is a difficult and complex process. Generally, existing software reliability models are applied to software testing data and then used to make predictions on the software failures and reliability in the field. In this paper, we present an improved exponential NHPP software reliability model in different development environments, and examine the goodness-of-fit of improved exponential model and other model based on two datasets. The results show that the proposed model fits significantly better than other NHPP software reliability model.

• Convergence Security Journal
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• v.12 no.6
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• pp.85-92
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• 2012

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. 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). For someone making a decision about when to market software, the conditional failure rate is an important variables. The finite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outliers, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical Process Control (SPC) can monitor the forecasting of software failure and there by 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 log Poission, log-linear and Parto distribution.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.697-704
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• 2005

Computer software has gradually become an indispensable elements in many aspects of our daily lives and an important factor in numerous systems. In recent years, it is not unusual that the software cost is more than the hardware cost in many situations. In addition to the costs of developing software, the repair cost resulting from the software failures are even more significant. In this paper, a cost model with warranty cost, time to remove each fault detected in the software system, and delivery delay cost is developed. We use a software reliability model based on non-homogeneous Poisson process (NHPP). We discuss the optimal release policies to minimize the expected total software cost. Numerical examples are provided to illustrate the results.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.4
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• pp.7-14
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• 2011

Infinite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rate per fault (hazard function). This infinite non-homogeneous Poisson process is model which reflects the possibility of introducing new faults when correcting or modifying the software. In this paper, polynomial hazard function have been proposed, which can efficiency application for software reliability. Algorithm for estimating the parameters used to maximum likelihood estimator and bisection method. Model selection based on mean square error and the coefficient of determination for the sake of efficient model were employed. In numerical example, log power time model of the existing model in this area and the polynomial hazard function model were compared using failure interval time. Because polynomial hazard function model is more efficient in terms of reliability, polynomial hazard function model as an alternative to the existing model also were able to confirm that can use in this area.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.10 no.2
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• pp.41-47
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• 2006

The Renewal process and the Non-homogeneous Poisson process (NHPP) process are probably the most popular models for describing the failure pattern of repairable systems. But both these models are based on too restrictive assumptions on the effect of the repair action. For these reasons, several authors have recently proposed point process models which incorporate both renewal type behavior and time trend. One of these models is the Modulated Power Law Process (MPLP). The Modulated Power Law Process is a suitable model for describing the failure pattern of repairable systems when both renewal-type behavior and time trend are present. In this paper we propose Bayes estimation of the next failure time after the system has experienced some failures, that is, Mean Time Between Failure for the MPLP model. Numerical examples illustrate the estimation procedure.

• Proceedings of the KSR Conference
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• 2001.05a
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• pp.347-358
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• 2001

In this paper, we apply NHPP model example to s/w process in order to get to know s/w reliability. The test is constructed by a test zig of commercial product loaded real embedded system s/w. It is established to s/w reliability prediction and estimation of real-time embedded system s/w. It is computed the prediction value of cumulative failures, the failure intensity, the reliability and the estimation value of MTTF, Failure Rate. To the more realization of high reliability in the real-time embedded system s/w, if the embedded system s/w is ensured to the test coverage and constructed to stable s/w process ＆ operating system, we can improve the performance and the reliability characteristic of the real-time embedded system s/w.

• 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.

• Proceedings of the Korean Reliability Society Conference
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• 2004.07a
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• pp.207-210
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• 2004

컴퓨터 소프트웨어는 이제 우리의 일상적인 삶에서 필수불가결한 요소이며 시스템의 운용에 중요한 요인이 되었다. 최근에 들어서는 소프트웨어 비용이 하드웨어 비용을 초과하게 되면서 소프트웨어를 개발하는데 필요한 비용과 더불어 소프트웨어 고장에 의한 비용의 중요성이 더 커지게 되었다. 본 논문에서는 Non-Homogeneous Poisson Process(NHPP)에 기초한 소프트웨어 비용 모형을 제안하려고 한다. 개발초기단계에서 출시 전까지의 소프트웨어 개발비용과 테스트비용, 출시이후의 보증기간동안의 제반비용, 소프트웨어 고장에 의한 위험비용 등을 포함하는 소프트웨어 비용 모형을 제안하고 소프트웨어의 최적 출시시기를 결정하는 효과적인 정책을 제시하려고 한다.