로그 파우어 강도함수에 근거한 소프트웨어 무한고장 NHPP 신뢰성 비용 모형에 관한 비교 연구
A Comparative Study on Infinite NHPP Reliability Cost Model Based on Intensity Function of Log Power Form
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초록
본 연구에서는 소프트웨어 제품 테스팅 과정에서 고장 수명분포의 강도함수를 고려한 소프트웨어 신뢰성 비용모형에 대하여 연구하였다. 소프트웨어 신뢰성 분야에서 많이 사용되는 로그 파우어 강도함수를 가진 소프트웨어 신뢰성장 모형에 대한 비교문제를 제시하였다. 소프트웨어 고장형태는 무한고장 비동질적인 포아송과정을 이용하고 모수추정법은 최우추정법을 이용하였다. 따라서 본 논문에서는 강도함수를 고려한 소프트웨어 비용모형 분석을 위하여 소프트웨어 고장시간자료를 적용하여 비교 분석하였다. 이 연구를 통하여 소프트웨어 개발자들은 소프트웨어 개발 비용을 파악 하는데 도움을 줄 수 있을 것으로 사료된다.
Abstract
With the increasing demand to deliver high-quality software, more accurate software reliability models and software cost models are required to estimate the optimal software release time and the cost of testing efforts. In this study, reliability software cost model considering intensity function based on life distribution from the process of software product testing was studied. The comparison problem of log power intensity function that is widely used in the field of reliability, reliability growth model, was presented. The software failure pattern was used infinite failure non-homogeneous Poisson process model, the parameters estimation using maximum likelihood estimation was conducted. For analysis of software cost model considering intensity function, in this research, software developers to identify software development cost some extent be able to help. In this study, software cost model, using the infinite non-homogeneous Poisson process(NHPP) failure, more software efficiently optimum delivery times can be predicted. The proposed model is the total number of defects in software release period after the operational software and software to be discovered during maintenance and the defects are not found in all software users should assume that. It's a real step error detection and error correction cost of removing all of the remaining operational steps to eliminate the error is lower than the cost increases, the operating time can be seen as costs increase. Therefore, the optimal discharge time of the software can be realistically predict beforehand.
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