• Title, Summary, Keyword: 대수형 학습효과

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The Assessing Comparative Study for Statistical Process Control of Software Reliability Model Based on Logarithmic Learning Effects (대수형 학습효과에 근거한 소프트웨어 신뢰모형에 관한 통계적 공정관리 비교 연구)

  • Kim, Kyung-Soo;Kim, Hee-Cheul
    • 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.

The Comparative Study for NHPP Software Reliability Model based on the Property of Learning Effect of Log Linear Shaped Hazard Function (대수 선형 위험함수 학습효과에 근거한 NHPP 신뢰성장 소프트웨어 모형에 관한 비교 연구)

  • Kim, Hee-Cheul;Shin, Hyun-Cheul
    • Convergence Security Journal
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    • v.12 no.3
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    • pp.19-26
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    • 2012
  • 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 log type hazard function 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 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 $R^2$(coefficient of determination).