total.plot: Plotting the mean value functions for all models

In Reliability: Functions for estimating parameters in software reliability models

Description

total.plot plots the mean value function for all models and the raw data into one window.

Usage

total.plot(duane.par1, duane.par2, lit.par1, lit.par2, lit.par3, mor.par1, 
           mor.par2, musa.par1, musa.par2, t, linear = T, xlab = "time", 
           ylab = "Cumulated failures and estimated mean value functions", 
           main = NULL)

Arguments

duane.par1	parameter value for rho for Duane model
duane.par2	parameter value for theta for Duane model
lit.par1	parameter value for theta0 for Littlewood-Verall model
lit.par2	parameter value for theta1 for Littlewood-Verall model
lit.par3	parameter value for rho for Littlewood-Verall model
mor.par1	parameter value for D for Moranda-Geometric model
mor.par2	parameter value for theta for Moranda-Geometric model
musa.par1	parameter value for theta0 for Musa-Okumoto model
musa.par2	parameter value for theta1 for Musa-Okumoto model
t	time between failure data
linear	logical. Should the linear or the quadratic form of the mean value function for the Littlewood-Verrall model be used of computation? If TRUE, which is the default, the linear form of the mean value function is used.
xlab	a title for the x axis
ylab	a title for the y axis
main	an overall title for the plot

Details

This function gives a plot of the mean value functions for all models. Here the estimated parameter values, which are obtained by using duane, littlewood.verall, moranda.geometric und musa.okumoto can be put in. Internally the functions mvf.duane, mvf.ver.lin, mvf.ver.quad, mvf.mor and mvf.musa are used to get the mean value functions for all models.

Value

A graph of the mean value functions for all models and of the raw data.

Author(s)

Andreas Wittmann andreas\[e-mail]

References

J.D. Musa, A. Iannino, and K. Okumoto. Software Reliability: Measurement, Prediction, Application. McGraw-Hill, 1987.

Michael R. Lyu. Handbook of Software Realibility Engineering. IEEE Computer Society Press, 1996. http://www.cse.cuhk.edu.hk/~lyu/book/reliability/

See Also

duane.plot, littlewood.verall.plot, moranda.geometric.plot, musa.okumoto.plot

Examples

# time between-failure-data from DACS Software Reliability Dataset
# homepage, see system code 1. Number of failures is 136.
t <- c(3, 30, 113, 81, 115, 9, 2, 20, 20, 15, 138, 50, 77, 24,
       108, 88, 670, 120, 26, 114, 325, 55, 242, 68, 422, 180,
       10, 1146, 600, 15, 36, 4, 0, 8, 227, 65, 176, 58, 457,
       300, 97, 263, 452, 255, 197, 193, 6, 79, 816, 1351, 148,
       21, 233, 134, 357, 193, 236, 31, 369, 748, 0, 232, 330,
       365, 1222, 543, 10, 16, 529, 379, 44, 129, 810, 290, 300,
       529, 281, 160, 828, 1011, 445, 296, 1755, 1064, 1783, 
       860, 983, 707, 33, 868, 724, 2323, 2930, 1461, 843, 12,
       261, 1800, 865, 1435, 30, 143, 108, 0, 3110, 1247, 943,
       700, 875, 245, 729, 1897, 447, 386, 446, 122, 990, 948,
       1082, 22, 75, 482, 5509, 100, 10, 1071, 371, 790, 6150,
       3321, 1045, 648, 5485, 1160, 1864, 4116)
      
duane.par1 <- duane(t)$rho
duane.par2 <- duane(t)$theta

lit.par1 <- littlewood.verall(t, linear = TRUE)$theta0
lit.par2 <- littlewood.verall(t, linear = TRUE)$theta1
lit.par3 <- littlewood.verall(t, linear = TRUE)$rho

mor.par1 <- moranda.geometric(t)$D
mor.par2 <- moranda.geometric(t)$theta

musa.par1 <- musa.okumoto(t)$theta0
musa.par2 <- musa.okumoto(t)$theta1

total.plot(duane.par1, duane.par2, lit.par1, lit.par2, lit.par3, mor.par1, 
           mor.par2, musa.par1, musa.par2, t, linear = TRUE, 
           xlab = "time (in seconds)", main = "all models")

Example output

Reliability documentation built on May 1, 2019, 9:22 p.m.