• Journal of Information Technology Applications and Management
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• 제24권2호
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• pp.71-80
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• 2017

Software reliability is one of the most elementary and important problems in software development In order to find the software failure occurrence, the instantaneous failure rate function in the Poisson process can have a constant, incremental or decreasing tendency independently of the failure time. In this study, we compared the reliability performance of the software reliability model using the parameters of Pareto life distribution with the intensity decreasing pattern and the shape parameter of Erlang life distribution with the intensity increasing and decreasing pattern in the software product testing. In order to identify the software failure environment, the parametric estimation was applied to the maximum likelihood estimation method. Therefore, in this paper, we compare and evaluate software reliability by applying software failure time data. The reliability of the Erlang and Pareto life models is shown to be higher than that of the Pareto lifetime distribution model when the shape parameter is higher and the Erlang model is more reliable when the shape parameter is higher. Through this study, the software design department will be able to help the software design by applying various life distribution and shape parameters, and providing basic knowledge using software failure analysis.

• 산업경영시스템학회지
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• 제15권25호
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• pp.53-62
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• 1992

Computer software users develop and utilize their application software by themselves since Processing methods are different by quantity and qualify of the information The developed model needs input data and error numbers generated during the testing phases. However. total error numbers of the existing model and each error time was needed as data for developing the new model. But, maximum likelihood estimation must be used to exponential model of binomial-type and estimating of parameters by using the searched data. Parameter estimation can be done with trial and error or simulation.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제12권3호
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• pp.153-160
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• 2008

A size-biased Poisson distribution is defined. Its characterization by using a recurrence relation for first order negative moment of the distribution is obtained. Different estimation methods for the parameter of the model are also discussed. R-Software has been used for making a comparison among the three different estimation methods.

• 한국IT서비스학회:학술대회논문집
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• 한국IT서비스학회 2003년도 추계학술대회
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• pp.616-623
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• 2003

Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However if we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set, eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.

• 통합자연과학논문집
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• 제7권3호
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• pp.193-199
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• 2014

As the requirement on the quality of the system has increased, the reliability is very important part in terms of enhance stability and to provide high quality services to customers. Many statistical models have been developed in the past years for the estimation of software reliability. We consider the functions for NHPP software reliability model and time series model in software failure data. We estimate parameters for the proposed models from three data sets. The values of SSE and MSE is presented from three data sets. We compare the predicted number of faults with the actual three data sets using the NHPP software reliability model and time series model.

• 디지털산업정보학회논문지
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• 제8권2호
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• pp.1-10
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• 2012

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure nonhomogeneous Poisson process models presented and the life distribution applied exponential and log shaped type hazard function. 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 could be confirmed. This paper, a failure data analysis 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 coefficient of determination.

• 디지털산업정보학회논문지
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• 제7권2호
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• pp.1-8
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• 2011

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure non-homogeneous Poisson process models presented and the life distribution applied extreme distribution which used to find the minimum (or the maximum) of a number of samples of various distributions. 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, 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 were efficient using the mean square error.

• Journal of Information Technology Applications and Management
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• 제23권4호
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• pp.117-125
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• 2016

Software reliability in the software development process is an important issue. In infinite failure non-homogeneous Poisson process software reliability models, the failure occurrence rates per fault. can be presented constant, monotonic increasing or monotonic decreasing pattern. In this paper, the reliability software cost model considering decreasing intensity function was studied in the software product testing process. The decreasing intensity function that can be widely used in the field of reliability using power law process, log-linear processes and Musal-Okumoto process were studied and the parameter estimation method was used for maximum likelihood estimation. In this paper, from the software model analysis, we was compared by applying a software failure interval failure data considering the decreasing intensity function The decreasing intensity function model is also efficient in terms of reliability in the arena of the conservative model can be used as an alternating model can be established. From this paper, the software developers have to consider life distribution by preceding information of the software to classify failure modes which can be gifted to support.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1721-1737
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• 2020

Human apparent age estimation has become a popular research topic and attracted great attention in recent years due to its wide applications, such as personal security and law enforcement. To achieve the goal of age estimation, a large number of methods have been pro-posed, where the models derived through the cumulative attribute coding achieve promised performance by preserving the neighbor-similarity of ages. However, these methods afore-mentioned ignore the geometric structure of extracted facial features. Indeed, the geometric structure of data greatly affects the accuracy of prediction. To this end, we propose an age estimation algorithm through joint feature selection and manifold learning paradigms, so-called Feature-selected and Geometry-preserved Least Square Regression (FGLSR). Based on this, our proposed method, compared with the others, not only preserves the geometry structures within facial representations, but also selects the discriminative features. Moreover, a deep learning extension based FGLSR is proposed later, namely Feature selected and Geometry preserved Neural Network (FGNN). Finally, related experiments are conducted on Morph2 and FG-Net datasets for FGLSR and on Morph2 datasets for FGNN. Experimental results testify our method achieve the best performances.

• 한국컴퓨터정보학회논문지
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• 제14권7호
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• pp.17-24
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• 2009

정보시스템에 대한 이용이 늘어남에 따라 소프트웨어 개발 요구와 개발 비용이 증가하게 되었다. 기존에는 통계적 알고리즘 기반의 회귀분석을 이용하여 소프트웨어 개발비용을 산정하였으나 오늘날은 기계학습 방법들이 많이 연구되고 있다. 본 논문에서는 기계학습 기술의 하나인 SVR를 사용하여 소프트웨어 비용을 산정하였고, 이 때 SVR에서 사용하는 파라미터들의 최적 조합을 면역계의 동작원리를 적용한 면역 알고리즘을 적용하여 최적 조합을 찾았다. 소프트웨어 비용산정을 위해 세대수, 기억세포수, 대립유전자수를 변경해 가면서 면역 알고리즘 기반의 SVR을 적용하였고, 그 실험 결과를 기존 연구된 다른 기계학습 방법과 비교 분석하였다.