Advanced SearchSearch Tips
The Study of Infinite NHPP Software Reliability Model from the Intercept Parameter using Linear Hazard Rate Distribution
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
The Study of Infinite NHPP Software Reliability Model from the Intercept Parameter using Linear Hazard Rate Distribution
Kim, Hee-Cheul; Shin, Hyun-Cheul;
  PDF(new window)
Software reliability in the software development process is an important issue. In infinite failure NHPP software reliability models, the fault occurrence rates may have constant, monotonic increasing or monotonic decreasing pattern. In this paper, infinite failures NHPP models that the situation was reflected for the fault occurs in the repair time, were presented about comparing property. Commonly, the software model of the infinite failures using the linear hazard rate distribution software reliability based on intercept parameter was used in business economics and actuarial modeling, was presented for comparison problem. The result is that a relatively large intercept parameter was appeared effectively form. The parameters estimation using maximum likelihood estimation was conducted and model selection was performed using the mean square error and the coefficient of determination. The linear hazard rate distribution model is also efficient in terms of reliability because it (the coefficient of determination is 90% or more) in the field of the conventional model can be used as an alternative model could be confirmed. From this paper, the software developers have to consider intercept parameter of life distribution by prior knowledge of the software to identify failure modes which can be able to help.
Software Reliability;NHPP;Laplace Trend Test;Linear Hazard Rate Distribution;
 Cited by
Gokhale, S. S. and Trivedi, K. S. A, "time/structure based software reliability model", Annals of Software Engineering. 8, pp. 85-121. 1999. crossref(new window)

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.

Yamada S, Ohba H. S-shaped software reliability modeling for software error detection. IEEE Transaction on Reliability 3, pp. 475-84, 1983.

Zhao M. Change-point problems in software and hardware reliability. Communication Stat Theory Methods 22(3), pp. 757-68, 1993. crossref(new window)

Shyur H-J. A stochastic software reliability model with imperfect debugging and change-point. J Syst Software 66(2), pp. 135-41, 2003. crossref(new window)

Pham H, Zhang X., "NHPP software reliability and cost models with testing coverage", Eur. J. Oper. Res, 145, pp.445-454, 2003.

Huang C-Y. Performance analysis of software reliability growth models with testing-effort and change-point. J Syst Software. 2005; 76:181-94. crossref(new window)

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. crossref(new window)

Hee-Cheul KIM, "The Assessing Comparative Study for Statistical Process Control of Software Reliability Model Based on Rayleigh and Burr Type", Journal of korea society of digital industry and information management, Volume 10, No.2, pp. 1-11,2014. crossref(new window)

J. F. Lawless. Statistical Models and Methods for Life time Data. John Wiley & Sons, New York, 1981.

Y. HAYAKAWA and G. TELFAR, "Mixed PoissonType Processes with Application in Software Reliability", Mathematical and Computer Modelling, 31, pp.151-156, 2000. crossref(new window)

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.