A NOTE ON THE ``MEAN VALUE'' SOFTWARE RELIABILITY MODEL

In this paper we study the Hausdorff approximation of the Heaviside step function ht0(t) by sigmoidal curve based on the ”mean value” software reliability model and find an expression for the error of the best approximation. AMS Subject Classification: 68M15, 68N30

[1]  Nikolay Pavlov,et al.  A NOTE ON THE YAMADA--EXPONENTIAL SOFTWARE RELIABILITY MODEL , 2018 .

[2]  Daisuke Satoh A Discrete Gompertz Equation and a Software Reliability Growth Model , 2000 .

[3]  A. M. Abouammoh,et al.  Reliability estimation of generalized inverted exponential distribution , 2009 .

[4]  Shunji Osaki,et al.  Software Reliability Growth Modeling: Models and Applications , 1985, IEEE Transactions on Software Engineering.

[5]  Shigeru Yamada,et al.  Discrete equations and software reliability growth models , 2001, Proceedings 12th International Symposium on Software Reliability Engineering.

[6]  Amrit L. Goel,et al.  Software Reliability Models: Assumptions, Limitations, and Applicability , 1985, IEEE Transactions on Software Engineering.

[7]  Tadashi Dohi,et al.  Gompertz software reliability model: Estimation algorithm and empirical validation , 2009, J. Syst. Softw..

[8]  William T. Shaw,et al.  The alchemy of probability distributions: beyond Gram-Charlier expansions, and a skew-kurtotic-normal distribution from a rank transmutation map , 2009, 0901.0434.

[9]  Shigeru Yamada A stochastic software reliability growth model with Gompertz curve (abstract) , 1992 .

[10]  Shaheda Akthar,et al.  Software Reliability Growth Model with Gompertz TEF and Optimal Release Time Determination by Improving the Test Efficiency , 2010 .

[11]  Nikolay Pavlov,et al.  SOME DETERMINISTIC RELIABILITY GROWTH CURVES FOR SOFTWARE ERROR DETECTION: APPROXIMATION AND MODELING ASPECTS , 2018 .

[12]  Transmuted Generalized Inverted Exponential Distribution , 2013 .

[13]  Shigeru Yamada,et al.  S-Shaped Reliability Growth Modeling for Software Error Detection , 1983, IEEE Transactions on Reliability.

[14]  Edwin Lughofer,et al.  Residual-based fault detection using soft computing techniques for condition monitoring at rolling mills , 2014, Inf. Sci..