Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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The Comparative Software Cost Model of Considering Logarithmic Fault Detection Rate Based on Failure Observation Time

로그형 관측고장시간에 근거한 결함 발생률을 고려한 소프트웨어 비용 모형에 관한 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 : 2013.09.02
  • Accepted : 2013.11.20
  • Published : 2013.11.28
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Abstract

In this study, reliability software cost model considering logarithmic fault detection rate based on observations from the process of software product testing was studied. Adding new fault probability using the Goel-Okumoto model that is widely used in the field of reliability problems presented. When correcting or modifying the software, finite failure non-homogeneous Poisson process model. For analysis of software cost model considering the time-dependent fault detection rate, the parameters estimation using maximum likelihood estimation of inter-failure time data was made. In this research, Software developers to identify the best time to release some extent be able to help is considered.

본 연구에서는 소프트웨어 제품 테스팅 과정에서 관측고장시간에 근거한 로그형 결함 발생률을 고려한 소프트웨어 신뢰성 비용 모형에 대하여 연구 하였다. 신뢰성 분야에서 많이 사용되는 Goel-Okumoto모형을 이용한 새로운 로그 형 결함 확률을 반영한 문제를 제시하였다. 수명분포는 유한고장 비동질적인 포아송과정을 이용하고 모수 추정법은 최우 추정법을 이용 하였다. 따라서 본 논문에서는 로그형 결함 발생률을 고려한 소프트웨어 비용모형 분석을 위하여 소프트웨어 고장 시간간격 자료를 적용하여 비교 분석하였다. 이 연구를 통하여 소프트웨어 개발자들은 방출최적시기를 파악 하는데 어느 정도 도움을 줄 수 있을 것으로 사료 된다.

Keywords

References

  1. 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
  2. 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.
  3. Yamada S, Ohba H., S-shaped software reliability modeling for software error detection", IEEE Trans. Reliab, 32, pp.475-484, 1983.
  4. 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
  5. 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
  6. Pham H, Zhang X., NHPP software reliability and cost models with testing coverage", Eur. J. Oper. Res, 145, pp.445-454, 2003.
  7. 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
  8. 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
  9. Kim Hee Cheul and Shin Hyun Cheul, The Comparative Study for NHPP Software Reliability Model based on the Property of Learning Effect of Log Linear Shaped Hazard Function, Convergence Security Journal, Vol. 12, No. 3, pp. 19-26, 2012.
  10. Ye Zhang and Kaigui Wu, Software Cost Model Considering Reliability and Time of Software in Use, Journal of Convergence Information Technology (JCIT), Volume 7, Number 13, pp. 135-142, 2012.
  11. J. F. Lawless, Statistical Models and Methods for Lifetime Data. John Wiley & Sons, New York, 1981.
  12. 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
  13. Y. HAYAKAWA and G. TELFAR., Mixed Poisson Type Processes with Application in Software Reliability, Mathematical and Computer Modeling, 31, pp. 151-156, 2000. https://doi.org/10.1016/S0895-7177(00)00082-0
  14. 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.
  15. Changjie Ma, Guochang Gu, Jing Zhao, A Novel Software Reliability Assessment Approach based on Neural Network in Network Environment, IJACT: International Journal of Advancements in Computing Technology, Vol. 4, No. 1, pp. 136-144, 2012. https://doi.org/10.4156/ijact.vol4.issue1.15

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