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A Performance Analysis of Software Reliability Model using Lomax and Gompertz Distribution Property

Affiliations

  • Department of Industrial and Management Engineering, Namseoul University, Korea, Republic of

Abstract


Background/Objectives: In this research, the comparative problems of a reliability model about Lomax and Gomperz distribution property were proposed. The maximum likelihood estimation and bisection method were used to the parameter estimation. Methods/Statistical Analysis: As special occasions, the model selection based on the Mean Square Error and coefficient of determination for the efficient model was conducted. Analysis of a failure time using real data set from the proposing reliability model (based on Lomax and Gomperz distribution) was worked. To obtain for the data reliability, the Laplace trend test was employed. Findings: In this study, the proposed Lomax than Gomperz distribution model is more efficient model in this area. The case of the higher shaping parameter of the Lomax distribution model is judged more reliable model in this field. Thus, Lomax distribution model can also be applied as a special model. Improvements: The software testing for the debugging to reduce cost in terms of the reliability from software is essential problem. From a research, the software developers must be considered for the growth model by the prior knowledge of the software to identify failure modes which can be able to help.

Keywords

Gomperz Distribution, Laplace Trend Test, Lomax Distribution, Mission Time, NHPP.

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References


  • Gokhale SS, Trivedi KS. A time/structure based software reliability model. Annals of Software Engineering. 1998; 8:85–121.
  • Goel AL, Okumoto K. Time dependent error - detection rate model for software reliability and other performance measure. IEEE Trans Reliability. 1979 Aug; R28(3):206–11.
  • Yamada S, Ohba H. S-shaped software reliability modeling for software error detection. IEEE Trans Reliab. 1983; 12:475–84.
  • Zhao M. Change-point problems in software and hardware reliability. Commun Stat Theory Methods. 1993 Jan; 22(3):757–68.
  • Shyur HJ. A stochastic software reliability model with imperfect debugging and change-point. J Syst Software. 2003 May; 66(2):135–41.
  • Pham H, Zhang X. NHPP software reliability and cost models with testing coverage. Eur J Oper Res. 2003; 145(2):445– 54.
  • Huang CY. Performance analysis of software reliability growth models with testing-effort and change-point. J Syst Software. 2005 Aug; 767(2):139–55.
  • Kuei-Chen C, Yeu-Shiang H, Tzai-Zang L. A study of software reliability growth from the perspective of learning effects. Reliability Engineering and System Safety. 2008; 93:1410–21.
  • Yoo TH. The Infinite NHPP software reliability model based on Monotonic Intensity Function. Indian Journal of Science and Technology. 2015 Jul; 8(14):1–7.
  • Kuo L, Yang TY. Bayesian computation of software reliability. Journal of the American Statistical Association. 1996; 91:763–73.
  • Univariate Distribution Relationships. 2015. Available from: http://www.math.wm.edu/~leemis/chart/ UDR/ PDFs/Lomax.pdf
  • Satya Prasad R, Sridevi G, Kumari KS. Assessing Pareto Type II Software Reliability using SPC. International Journal of Computer Applications. 2013 Jan; 62(3):17–21.
  • Hayakawa Y, Telfar G. Mixed poisson-type processes with application in software reliability. Mathematical and Computer Modelling. 2000 May–Jun; 31(10-12):151–6.
  • Kim HC. The property of learning effect based on delayed software S-Shaped reliability model using Finite NHPP Software cost model. Indian Journal of Science and Technology. 2015 Dec; 8(34):1–7.

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