Abstract
Software failure time have been proposed in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model trend analysis was developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offers information of outline content. In this paper, we discuss failure time case of failure time censoring, and predict the future failure time using nonlinear regression models (growth, Logistic and weighted type) which error terms for each other are different. The proposed prediction method used the failure time for the prediction using nonlinear regression model. Model selection, using the coefficient of determination and the mean square error, were presented for effective comparison.
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References
Gokhale SS, Trivedi KS (1999) A time/structure based software reliability model. Annal Softw Eng 8:85–121
Huang C-Y (2005) Performance analysis of software reliability growth models with testing-effort and change-point. J Syst Softw 76:181–194
Kuei-Chen C, Yeu-Shiang H, Tzai-Zang L (2008) A study of software reliability growth from the perspective of learning effects. Reliab Eng Syst Saf 93:1410–1421
Kim H-C, Shin H-C (2012) The study for software future forecasting failure time using curve regression analysis. Korea Convergence Secur Assoc 12(3):115–121
Kim H-C, Park H-K (2009) Exponentiated exponential software reliability growth model. Int J Adv Comput Technol 1(2):57–64
Kim H-C, Shin H-C (2008) The study for software future forecasting failure time using ARIMA AR(1). Korea Inf Assur Assoc 8(2):36–40
Kim H-C, Shin H-C (2008) The study for comparative analysis software future time using EWMA control chart. Korea Inf Assur Assoc 8(3):33–39
Kim H-C, Shin H-C (2011) The study for software future forecasting failure time series analysis. Korea Inf Assur Assoc 11(3):19–24
Kim H-C (2010) Introduction to regression analysis. Biz-Press, pp 131–137
Hayakawa Y, Telfar G (2000) Mixed poisson-type processes with application in software reliability. Math Comput Model 31:151–156
Kanoun K, Laprie JC (1996) Handbook of software reliability engineering. In: Lyu MR (ed) Chapter trend analysis. McGraw-Hill, New York, pp 401–437
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Funding for this paper was provided by Namseoul University.
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© 2013 Springer Science+Business Media Dordrecht
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Ra, YS., Kim, HC. (2013). Study for Predict of the Future Software Failure Time Using Nonlinear Regression. In: Kim, K., Chung, KY. (eds) IT Convergence and Security 2012. Lecture Notes in Electrical Engineering, vol 215. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5860-5_133
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DOI: https://doi.org/10.1007/978-94-007-5860-5_133
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