Journal of Korea Society of Digital Industry and Information Management (디지털산업정보학회논문지)

Korea Society of Digital Industry and Information Management (디지털산업정보학회)
권호 표지
DOI QR코드

DOI QR Code

A Comparative Study of Software Reliability Model Considering Log Type Mean Value Function

로그형 평균값함수를 고려한 소프트웨어 신뢰성모형에 대한 비교연구

  • 신현철 (백석문화대학교 인터넷 정보학부) ;
  • 김희철 (남서울대학교 산업경영공학과)
  • Received : 2014.11.12
  • Accepted : 2014.12.08
  • Published : 2014.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

Software reliability in the software development process is an important issue. Software process improvement helps in finishing with reliable software product. Infinite failure NHPP software reliability models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, proposes the reliability model with log type mean value function (Musa-Okumoto and log power model), which made out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on mean square error (MSE) and coefficient of determination($R^2$), for the sake of efficient model, was employed. Analysis of failure using real data set for the sake of proposing log type mean value function was employed. This analysis of failure data compared with log type mean value function. In order to insurance for the reliability of data, Laplace trend test was employed. In this study, the log type model is also efficient in terms of reliability because it (the coefficient of determination is 70% or more) in the field of the conventional model can be used as an alternative could be confirmed. From this paper, software developers have to consider the growth model by prior knowledge of the software to identify failure modes which can be able to help.

Keywords

References

  1. Musa, J. D, Iannino, A. and Okumoto, K. "Software Reliability: Measurement, Prediction, Application," McGraw Hill, New York, 1987, pp. 289-291.
  2. L. Kuo, and T. Y. Yang, Bayesian Computation of Software Reliability, Journal of the American Statistical Association, Vol. 91, 1996, pp. 763-773. https://doi.org/10.1080/01621459.1996.10476944
  3. Gokhale, S. S. and Trivedi, K. S. "A time / structure based software reliability model," Annals of Software Engineering. 8, 1999, pp. 85-121. https://doi.org/10.1023/A:1018923329647
  4. Goel AL, Okumoto K, "Timedependent fault detection rate model for software and other performance measures," IEEE Trans Reliab. 28, pp. 206-11, 1978.
  5. Yamada S, Ohba H. "Sshaped software reliability modeling for software error detection," IEEE Trans Reliab, 32, 1983, pp. 475-484.
  6. Zhao M. "Changepoint problems in software and hardware reliability," Commun. Stat Theory Methods, 22(3), 1993, pp. 757-768. https://doi.org/10.1080/03610929308831053
  7. Shyur HJ. "A stochastic software reliability model with imperfect debugging and changepoint," J.syst. Software 66, 2003, pp. 135-141. https://doi.org/10.1016/S0164-1212(02)00071-7
  8. Pham H, Zhang X. "NHPP software reliability and cost models with testing coverage," Eur J. Oper Res, 145, 2003, pp. 445-454.
  9. Huang CY. "Performance analysis of software reliability growth models with testingeffort and changepoint," J.syst Software 76, 2005, pp. 181-194. https://doi.org/10.1016/j.jss.2004.04.024
  10. 김희철, "Rayleigh형과 Burr형 NHPP 소프트웨어 신뢰모형에 관한 통계적 공정관리 접근방법 비교연구," 디지털산업정보학회논문지, 제10권, 제2호, 2014, pp. 1-11.
  11. 김희철, "NHPP 극값 분포 소프트웨어 신뢰모형에 대한 학습효과 기법 연구," 디지털산업정보학회 논문지, 제 7권, 제 2호, 2011, pp. 1-8.
  12. 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, 2013, pp. 132-139.
  13. Kuo, L. and Yang, T. Y, "Bayesian Computation of Software Reliability," Journal of the American Statistical Association, Vol. 91, 1996, pp. 763-773. https://doi.org/10.1080/01621459.1996.10476944
  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, 2008, pp. 1410-1421. https://doi.org/10.1016/j.ress.2007.11.004
  15. 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. 2013, pp. 132-139.
  16. J. D. Musa, and K. Okumoto, "A logarithmic Poisson execution time model for software reliability measurement," Proc. 7th Inf. Conf. on Software Engineering, 23@238, 1984.
  17. K. Venkata Subba Reddy and Dr. B. Raveendra Babu, "A Log Based Approach for Software Reliability Modeling," International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 2, February 2014, pp. 49-51.
  18. Vincent Almering, Michiel van Genuchten, and Ger Cloudt, Peter J. M. Sonnemans, Software Reliability Growth Models in Practice, IEEE SOFTWARE, 2007, pp. 82-887.
  19. Tae-Jin Yang and Hee-Cheul Kim, "The Comparative Software Infinite NHPP Reliability Cost Model Based on Intensity Function of Log Power Form," Journal of The Korea Knowledge Information Technology Society(JKKITS), Vol. 9, No. 1, February 2014, pp. 22-29.
  20. Y. HAYAKAWA and G. TELFAR, Mixed PoissonType 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
  21. K. Kanoun, J. C. Laprie, "Handbook of Software Reliability Engineering," M. R. Lyu, Editor, chapter Trend Analysis. McGrawHill New York, NY, 1996, pp. 401-437.