Measuring Software Reliability: A Trend Using Machine Learning Techniques - Complex System Modelling and Control Through Intelligent Soft Computations - page 810

Appendix: Sample Snap Code for Machine Learning Classi er

Used by Software Fault Data Set of Billing System

## A Test Classi er

x< rnorm(100, mean = 5)

probplot(x)
## the same with horizontal tickmarks at the y-axis
opar <

-

par( “ las ” )

-

par(las = 1)
probplot(x)
## this should show the lack of fit at the tails

probplot(x, )

## for increasing degrees of freedom the t-distribution converges to rbridge
## normal
probplot(x, qt, df = 1) probplot(x, qt, df = 3) probplot(x, qt, df = 10)
probplot(x, qt, df = 100) ## manually add the line through the quartiles
p<

“ qunif ”

probplot(x, line = FALSE)
lines(p, col =

-

, lty = 2, lwd = 2)
## Make the line at prob = 0.5 red
lines(p, h = 0.5, col =

“ green ”

)
### The following use the estimated distribution given by the green
### line:
## What is the probability that x is smaller than 7?
lines(p, v = 7, bend = TRUE, col =

“ red ”

“ blue ” )

“ ”

## Median and 90lines(p, h = 0.5, col = red , lwd = 3, bend = TRUE)

“ ”

lines(p, h = c(0.05, 0.95), col = red , lwd = 2, lty = 3, bend = TRUE)

par(opar)
attach(Sample data Set)
## classification mode
# default with factor response:
model <

., data = iris)
# alternatively the traditional interface:
x<

svm(Species

- *

subset(iris, select = Species)

- −

y< Species

model <

-

svm(x, y)
print(model)
summary(model)
# test with train data
pred <

-

predict(model, x)
# (same as:)
pred <

-

-

fitted(model)