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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 (Dept. of Internet information, BaekSeok Culture University) ;
  • Kim, Hee-Cheul (Dept. of Industrial & Management Engineering, Namseoul University)
  • 김경수 (백석문화대학교 인터넷 정보학부) ;
  • 김희철 (남서울대학교 산업경영공학과)
  • Received : 2013.10.08
  • Accepted : 2013.12.20
  • Published : 2013.12.28

Abstract

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.

Keywords

Logarithmic Learning Effects;Non-Homogeneous Poisson Process;Statistical Process Control;Mean Value Function;Laplace trend test

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