디지털융복합연구 (Journal of Digital Convergence)

  • 제12권12호
  • /
  • Pages.277-284
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  • 2014
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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레일리분포를 이용한 유한고장과 무한고장 소프트웨어 신뢰성 모형에 대한 비교연구

The Comparative Study for Software Reliability Model Based on Finite and Infinite Failure Property using Rayleigh Distribution

  • 김경수 (백석문화대학교 인터넷 정보학부) ;
  • 김희철 (남서울대학교 산업경영공학과)
  • Kim, Kyung-Soo (Dept. of Internet information, BaekSeok Culture University) ;
  • Kim, Hee-Cheul (Dept. of Industrial & Management Engineering, Namseoul University)
  • 투고 : 2014.09.15
  • 심사 : 2014.12.20
  • 발행 : 2014.12.28
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⟨ 이전 논문 다음 논문 ⟩

초록

소프트웨어 고장분석을 위한 비동질적인 포아송과정에서 결함당 고장발생률이 상수이거나, 단조 증가 또는 단조 감소하는 패턴을 가질 수 있다. 본 논문에서는 결함의 기대값을 가정하는 유한고장소프트웨어 NHPP모형과 수리시점에서도 고장이 발생할 상황을 반영하는 무한고장 NHPP모형들을 비교 제시하였다. 소프트웨어 신뢰성분야에서 많이 인용되는 레일리분포를 이용한 유한고장과 무한고장 소프트웨어 신뢰성모형에 대한 비교문제를 탐색한 결과 무한고장모형이 유한고장모형보다 효율적으로 나타났다. 이러한 비교문제를 위하여 모수추정은 최우추정법을 이용하였다. 이 연구를 통하여 소프트웨어 개발자에게 소프트웨어 고장현상을 파악하는데 어느 정도 도움을 줄 수 있을 것으로 사료 된다.

The NHPP software reliability models for failure analysis can have, in the literature, exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, finite failure NHPP models that assuming the expected value of the defect and infinite failures NHPP models that repairing software failure point in time reflects the situation, were presented for comparing property. Commonly used in the field of software reliability based on Rayleigh distribution software reliability model finite failures and infinite failures were presented for comparison problem. As a result, infinite fault model is effectively finite fault models, respectively. The parameters estimation using maximum likelihood estimation was conducted. In this research, can be able to help software developers for considering software failure property some extent.

키워드

참고문헌

  1. L. Kuo and T. Y. Yang, Bayesian Computation of Software Reliability, Journal of the American Statistical Association, Vol.91, pp. 763-773, 1996. https://doi.org/10.1080/01621459.1996.10476944
  2. Gokhale, S. S. and Trivedi, K. S. A time/structure based software reliability model, Annals of Software Engineering, 8, pp. 85-121. 1999. https://doi.org/10.1023/A:1018923329647
  3. Goel A L, Okumoto K, Time-dependent fault detection rate model for software and other performance measures, IEEE Trans. Reliab., 28, pp.206-11, 1978.
  4. Yamada S, Ohba H., S-shaped software reliability modeling for software error detection", IEEE Trans. Reliab., 32, pp.475-484, 1983.
  5. Zhao M., Change-point problems in software and hardware reliability", Commun. Stat. Theory Methods, 22(3), pp.757-768, 1993. https://doi.org/10.1080/03610929308831053
  6. Shyur H-J., A stochastic software reliability model with imperfect debugging and change-point, J. Syst. Software 66, pp.135-141, 2003. https://doi.org/10.1016/S0164-1212(02)00071-7
  7. Pham H, Zhang X., "NHPP software reliability and cost models with testing coverage", Eur. J. Oper. Res., 145, pp.445-454, 2003.
  8. Huang C-Y., Performance analysis of software reliability growth models with testing-effort and change-point, J. Syst. Software 76, pp. 181-194, 2005. https://doi.org/10.1016/j.jss.2004.04.024
  9. Kuei-Chen, C., Yeu-Shiang, H., and Tzai-Zang, L., A study of software reliability growth from the perspective of learning effects, Reliability Engineering and System Safety 93, pp. 1410-1421, 2008. https://doi.org/10.1016/j.ress.2007.11.004
  10. Hee-Cheul KIM, "The Comparative Study of NHPP Half-Logistic Distribution Software Reliability Model using the Perspective of Learning Effects", Journal of Next Generation Information Technology, Vol. 4, No. 8, pp. 132-139, 2013.
  11. Hee-Cheul KIM, "The Comparative Study of NHPP Delayed S-Shaped and Extreme Value Distribution Software Reliability Model using the Perspective of Learning Effects", International Journal of Advancements in Computing Technology, Vol. 5, No.9, pp. 1210-1218, 2013. https://doi.org/10.4156/ijact.vol5.issue9.143
  12. Gokhale, S. S. and Trivedi, K. S. "A time / structure based software reliability model," Annals of Software Engineering, 8, pp. 85-121, 1999. https://doi.org/10.1023/A:1018923329647
  13. Kyung-Soo Kim, Hee-Cheul Kim, The Comparative Study of Software Optimal Release Time of Finite NHPP Model Considering Property of Nonlinear Intensity Function, The Journal of Digital Policy & Management, 11(1): 159-166, Sep, 2013. https://doi.org/10.14400/JDPM.2013.11.9.159
  14. Kuei-Chen. C, Yeu-Shiang. H, and Tzai-Zang. L, A study of software reliability growth from the perspective of learning effects, Reliability Engineering and System Safety 93, pp. 1410-1421, 2008. https://doi.org/10.1016/j.ress.2007.11.004
  15. SIMOS M. and GEORGE I., "TESTS OF FIT FOR THE RAYLEIGH DISTRIBUTION BASED ON THE EMPIRICAL LAPLACE TRANSFORM," Ann. Inst. Statist. Math. Vol. 55, No. 1, pp. 137-151, 2003.
  16. Kim, Hee-cheul, "The Assessing Comparative Study for Statistical Process Control of Software Reliability Model Based on Rayleigh and Burr Type", Journal of the Korea Society of Digital Industry and Information Management, pp. 1-11, 2014. https://doi.org/10.17662/ksdim.2014.10.1.001
  17. Y. HAYAKAWA and G. TELFAR, "Mixed Poisson - Type Processes with Application in Software Reliability", Mathematical and Computer Modelling, 31, pp. 151-156, 2000. https://doi.org/10.1016/S0895-7177(00)00082-0
  18. Kim, Hee Cheul, "The Comparative Study of Software Optimal Release Time of Finite NHPPModel Considering Half-Logistic and Log-logistic Distribution Property", The Journal of Korea Society of Digital Industry and Information, 9(2):1-10, June, 2013.
  19. K. Kanoun and J. C. Laprie, Handbook of Software Reliability Engineering, M.R.Lyu, Editor, chapter Trend Analysis. McGraw-Hill New York, NY, pp. 401-437, 1996.