Abstract
Nonhomogeneous
poisson
process
based
software
reli ability
growth
models
are
generally
classified
into
two
groups.
The
first
group
contains
models,
which
use
the
machine
execution
time
or
calendar
time
as
a
un it
of
fault
detection/removal
period.
Such
models
are
called
co ntinuous
time
models.
The
second
group
contains
mod els,
which
use
the
number
of
test
occasions/cases
as
a
unit
of
fault
d etection
period.
Such
models
are
called
discrete
ti me
models,
since
the
unit
of
software
fault
detection
period
is
countable.
A
large
number
of
models
have
been
developed
in
the
f irst
group
while
there
are
fewer
in
the
second
group.
Discrete
time
models
in
software
reliability
are
important
and
a
little
effort
has
been
made
in
this
direction.
In
this
paper,
we
develop
two
discr ete
time
SRGMs
using
probability
generating
functio n
for
the
software
failure
occurrence
/
fault
detection
phenomenon
bas ed
on
a
NHPP
namely,
basic
and
extended
models.
The
basic
model
exploits
the
fault
detection/removal
rate
during
th e
initial
and
final
test
cases.
Whereas,
the
extended
model
incorporates
fault
generation
and
imperfect
debugging
with
learning.
A ctual
software
reliability
data
have
been
used
to
demonstrate
the
proposed
models.
The
results
are
fairly
encouraging
in
terms
of
goodness-of-fit
and
predictive
validity
criteria
due
to
applicability
and
flexibility
of
the
proposed
model s
as
they
can
capture
a
wide
class
of
reliability
growth
curves
ranging
from
purely
exponential
to
highly
S-shaped.


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[18] Xie M., Software Reliability Modelling , World Scientific, New York, 1991. Omar Shatnawi received his PhD, in computer science and his MSc in operational research from University of delhi in 2004 and 1999, respectively. Currently, he is head of Department of Information Systems at al!Bayt University. His research interests are in software engineering, wit h an emphasis on improving software reliability and dependability.