• 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.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.25-32
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• 2017

For repairable software systems, the Mean Time Between Failure (MTBF) is used as a measure of software system stability. Therefore, the evaluation of software reliability requirements or reliability characteristics can be applied MTBF. In this paper, we want to compare MTBF in terms of parameter estimation using Makeham life distribution. The parameter estimates used the least square method which is regression analyzer method and the maximum likelihood method. As a result, the MTBF using the least square method shows a non-decreased pattern and case of the maximum likelihood method shows a non-increased form as the failure time increases. In comparison with the observed MTBF, MTBF using the maximum likelihood estimation is smallerd about difference of interval than the least square estimation which is regression analyzer method. Thus, In terms of MTBF, the maximum likelihood estimation has efficient than the regression analyzer method. In terms of coefficient of determination, the mean square error and mean error of prediction, the maximum likelihood method can be judged as an efficient method.