• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.183-191
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• 2010

Decision problem called an optimal release policies, after testing a software system in development phase and transfer it to the user, is studied. The applied model of release time exploited infinite non-homogeneous Poisson process. This infinite non-homogeneous Poisson process is a model which reflects the possibility of introducing new faults when correcting or modifying the software. The failure life-cycle distribution used mixture which has various intensity, if the system is complicated. Thus, software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement becomes an optimal release policies. In a numerical example, after trend test applied and estimated the parameters using maximum likelihood estimation of inter-failure time data, estimated software optimal release time.

• International Journal of Reliability and Applications
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• v.7 no.2
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• pp.155-166
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• 2006

Many software reliability growth models (SRGMs) based on nonhomogeneous Poisson process (NHPP) have been developed and applied in practice. NHPP SRGMs are characterized by their mean value functions. Mean value functions are usually derived from differential equations representing the fault detection/removal process during testing. In this paper such differential equations are regarded as frameworks for generating mean value functions. Currently available frameworks are theoretically discussed with respect to capability of representing the fault detection/removal process. Then two general frameworks are proposed.

• Journal of Integrative Natural Science
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• v.6 no.2
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• pp.111-117
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• 2013

We propose a mean value function for software failures in NHPP software reliability model. And we deal with the maximum likelihood estimation and the least squares estimation in the proposed mean value function. The explicit mean value function solution for the proposed model is presented by MLE and LSE in two data sets. The values of SSE and MSE is presented in two data sets by MLE and LSE. We compare the predicted number of faults with the actual two data sets using the proposed mean value function.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.278-284
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• 2016

Software reliability in the software development process is an important issue. In infinite failure NHPP software reliability models, the fault occurrence rates may have constant, monotonic increasing or monotonic decreasing pattern. In this paper, infinite failures NHPP models that the situation was reflected for the fault occurs in the repair time, were presented about comparing property. Commonly, the software model of the infinite failures using the linear hazard rate distribution software reliability based on intercept parameter was used in business economics and actuarial modeling, was presented for comparison problem. The result is that a relatively large intercept parameter was appeared effectively form. The parameters estimation using maximum likelihood estimation was conducted and model selection was performed using the mean square error and the coefficient of determination. The linear hazard rate distribution model is also efficient in terms of reliability because it (the coefficient of determination is 90% or more) in the field of the conventional model can be used as an alternative model could be confirmed. From this paper, the software developers have to consider intercept parameter of life distribution by prior knowledge of the software to identify failure modes which can be able to help.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.67-73
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• 2014

We consider the mean value function for NHPP software reliability model and time series regression model in software failure data. We estimate parameters for the proposed models from two data sets. The values of SSE and MSE is presented from two data sets. We compare the predicted number of faults with the actual two data sets using the mean value function and regression curve.

• 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 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.5
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• pp.496-502
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• 2016

In this study, we investigated the comparative NHPP software model based on learning techniques that operators in the process of software testing and development of software products that can be applied to software test tool. The life distribution was applied Makeham distribution based on finite fault NHPP. 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 larger than automatic error that is usually well-organized model could be established. This paper, a trust characterization of applying using time among failures and parameter approximation using maximum likelihood estimation, after the effectiveness of the data through trend examination model selection were well-organized using the mean square error and $R^2$. From this paper, the software operators must be considered life distribution by the basic knowledge of the software to confirm failure modes which may be helped.

• Convergence Security Journal
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• v.11 no.6
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• pp.73-80
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• 2011

In this study, software products developed in the course of testing, software managers in the process of testing software and tools for effective learning effects perspective has been studied using the NHPP software. The delayed software S-shaped reliability model applied to distribution was based on finite failure NHPP. 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 automatic error that is generally efficient model could be confirmed. This paper, 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 were efficient using the mean square error and $R^2$(coefficient of determination).

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.5
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• pp.345-353
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• 2015

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 parameter inference for software reliability models based on finite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision to market software, the conditional failure rate is an important variables. In this case, finite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outlier, 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 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, proposed a control mechanism based on NHPP using mean value function of polynomial hazard function.