Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
권호 표지

Analysis of Software Reliability Growth Model with Gamma Family Distribution

감마족 분포를 이용한 소프트웨어 신뢰 성장 모형의 분석

  • Kan, Kwang-Hyun (Dept. of Information Communications & Computer Networks, Hallym Sungsim college) ;
  • Jang, Byeong-Ok (Dept. of Computer Internet and Information Major, Korea Nazarene University) ;
  • Kim, Hee-Cheul (Dept. of Industrial Information System Engineering, Namseoul University)
  • 간광현 (한림성심대학 정보통신네트워크과) ;
  • 장병옥 (나사렛대학교 인터넷정보과) ;
  • 김희철 (남서울대학교 산업정보시스템공학부)
  • Published : 2005.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

Finite failure NHPP models proposed in the literature exhibit is either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. For the sake of proposing shape parameter of the Gamma family distribution, used the special pattern. Data set, where the underlying failure process could not be adequately described by the knowing models, which motivated the development of the Gamma or Weibull model and Gompertz model. Analysis of failure data set that led us to the Gamma or Weibull model and Gompertz model using arithmetic and Laplace trend tests, bias tests was presented in this Paper.

본 연구에서는 유한고장 비동질적인 포아송 과정 모형에서 결함당 고장 발생률이 상수이거나, 단조 증가 또는 단조 감소하는 패턴을 가질수 있다. 감마족 분포를 적용하여 고장발생률에 대한 특징을 알아보았고 감마족 분포는 형상 모수의 선택에 따라 다양한 모형으로 유도 될 수 있다. 따라서 본 연구는 형상모형에 근거한 감마 또는 와이블 그리고 곰페르츠 모형을 제시하여 신뢰도 분석 결과를 나열하였고 모형 선택 및 자료 분석을 위하여 산술과 라플라스 검정과 편차 자승합 등을 이용하였다.

Keywords

References

  1. IEEE Trans. on Software Engineering v.SE-12 no.9 Evaluation of competing Software Reliability Predections Abdel-Ghally, A.A.;Chan, P.Y.;Littlewood, B.
  2. Handbook of Software Reliability Engineering, chapter Software Reliability Modeling Survery Farr, W.;Lyu, M.R.(ed.)
  3. IEEE Trans. on Reliability v.41 no.4 Optimal Properties of the Laplace Trend Test for Software-Reliability Models Gauodin, O.
  4. IEEE Trans. on Software Engineering v.SE-11 no.12 Software Reliability Models: Assumptions, Limitations and Applicability Goel, A.L.
  5. IEEE Trans. on Reliability v.R-28 no.3 Time-Dependent Error-Detection Rate Models for Software Reliability and Other Performance Measures Goel, A.L.;Okumoto, K.
  6. Annals of Software Engineering A time/structure based software reliability model Gokhale, S.S.;Trivedi, K.S.
  7. Handbook of Software Reliability Engineering;chapter Trend Analysis Kanoun, K.;Laprie, J.C.;Lyu, M.R.(ed.)
  8. Journal of the American Statistical Association v.91 Bayesian Computation of Software Reliability Kuo, L.;Yang, T.Y.
  9. Statistical Models and Methods for Lifetime Data Lawless, J.F.
  10. Handbook of Software Reliability Engineering;chapter Introduction Lyu, M.R.;Lyu, M.R.(ed.)
  11. Statistical Methods for Reliability Data Meeker, W.Q.;Escobar, L.A.
  12. Software Reliability: Measurement, Prediction, Application Musa, J.D.;Iannino, A.;Okumoto, K.
  13. IEEE Trans. on Software Engineering v.SE-1 no.1 A Theory of Software Reliability and its Application Musa, J.D.
  14. Handbook of Software Reliability Engineering;chapter Software Reliability Measurement Experience Nikora, A.P.;Lyu, M.R.;Lyu, M.R.(ed.)
  15. IEEE Trans. on reliability v.48 no.2 A General Imperfect-Software Debugging Model with S-Shaped Fault-Detection Rate Pham, H.;Nordmann, L.;Zhang, X.
  16. Moden Applied Biostatistical Methods Using S-Plus Selvin, S.
  17. Statistical inference Rohatgi, V.K.
  18. IEEE Trans. on Reliability v.R-32 no.5 S-Shaped Reliability Growth Modeling for Software Error Detection Yamada, S.;Ohba, M.;Osaki, S.
  19. answers.com-Weibull distribution