Int
ern
at
i
onal
Journ
al of Ele
ctrical
an
d
Co
mput
er
En
gin
eeri
ng
(IJ
E
C
E)
Vo
l.
8,
No.
6,
Decem
ber
20
18,
pp. 4
735~
474
4
IS
S
N: 20
88
-
8708, DO
I: 10
.11
591/ijece
.v8i6
.
pp4735
-
474
4
4735
Journ
al h
om
e
page
:
http:
//
ia
es
core
.c
om/
journa
ls
/i
ndex.
ph
p/IJECE
PORM:
Predi
ctive Opti
mizati
on
of Risk
Manag
em
ent to
C
ont
ro
l
Un
certai
nty Prob
lems in S
oftware
Enginee
ring
Sa
lm
a
Fir
d
os
e
1
,
L.
Manj
unath
Rao
2
1
Bharathia
r Uni
ver
sit
y
, In
dia
2
Dep
a
rtm
ent
of MC
A,
Dr.
Ambed
ka
r
I
ns
ti
tut
e of Tec
hnolog
y, I
nd
ia
Art
ic
le
In
f
o
ABSTR
A
CT
Art
ic
le
history:
Re
cei
ved
Ma
r
6
, 2
01
8
Re
vised
Jun
6
,
201
8
Accepte
d
J
ul
22
, 2
01
8
Irre
spec
t
ive
of
d
iffe
ren
t
rese
ar
ch
-
base
d
appr
oa
ch
es
towar
d
risk
ma
nage
m
ent,
deve
lop
ing
a
pr
ec
ise
m
odel
to
wards
risk
m
ana
gement
is
fou
nd
to
be
a
computat
ion
al
l
y
cha
l
le
nging
ta
s
k
owing
to
cr
it
i
ca
l
and
vagu
e
d
efi
nition
o
f
the
origi
n
at
ion
o
f
the
proble
m
s. This
rese
ar
ch
work i
ntroduces a
m
odel
ca
l
led
as
PR
OM
i.
e.
Predic
t
ive
Optimiza
t
ion
of
Risk
Man
age
m
ent
with
t
h
e
per
spec
t
ive
of
software
eng
ine
er
i
ng.
The
signif
ic
a
nt
cont
ribu
ti
on
o
f
PO
RM i
s
to
offe
r
a
rel
ia
b
le
computat
ion
of
risk
ana
l
y
sis
b
y
conside
r
ing
gene
ra
li
z
e
d
pra
ctical
sc
ena
r
io
of
softwar
e
developm
ent
pra
ctice
s
in
In
form
at
ion
Te
chno
log
y
(IT
)
in
dustr
y
.
Th
e
p
roposed
PO
RM
s
y
stem
is
al
so
d
esigne
d
and
equi
pped
wi
th
b
et
t
er
risk
fa
ct
or
assess
m
ent
with
an
a
id
of
m
ac
hi
ne
learni
n
g
appr
oac
h
withou
t
havi
ng
m
ore
i
nvolve
m
ent
of
itera
t
ion.
Th
e
stud
y
out
come
show
s
tha
t
PO
R
M
sy
st
em
offe
rs
computat
ion
al
l
y
cost
ef
fe
ct
iv
e
ana
l
y
sis
of
risk
factor
as
assess
ed
with
resp
ec
t
to
di
ffe
r
ent
qual
ity
s
ta
nd
ard
s
of
object
orie
nt
ed
s
y
s
te
m
invol
ved
in eve
r
y
sof
twar
e
project
s.
Ke
yw
or
d:
Ri
sk
fact
or
s
Ri
sk
m
anag
em
ent
So
ft
war
e
en
gine
erin
g
So
ft
war
e
pr
oj
e
ct
s
So
ft
war
e
ris
k
Un
ce
rtai
nty
Copyright
©
201
8
Instit
ut
e
o
f Ad
vanc
ed
Engi
n
ee
r
ing
and
S
cienc
e
.
Al
l
rights re
serv
ed
.
Corres
pond
in
g
Aut
h
or
:
Sal
m
a Firdose
,
Bharathia
r Un
i
ver
sit
y,
Coim
bator
e, T
a
m
il
nad
u
, I
nd
i
a
.
Em
a
il
:
salma
f.
phd
@g
m
ai
l.com
1.
INTROD
U
CTION
W
it
h
the
i
ncrea
sing
race
to
wards
offer
i
ng
qual
it
y
delivery
of
s
of
t
w
are
pro
j
ect
s,
t
he
s
of
twa
re
dev
el
op
m
ent
te
a
m
in
va
rio
us
IT
i
ndus
tr
ie
s
is
in
c
on
ti
nu
ous
ex
plorat
ion
pro
cess
f
or
su
c
h
e
ffec
ti
ve
dev
el
op
m
ent
m
et
ho
dolo
gi
es.
At
pre
sent
there
are
va
rio
us
sta
nd
a
rd
s
of
t
war
e
dev
e
lop
m
ent
m
et
ho
dolo
gies
[1
]
,
[2
]
as
we
ll
as
qu
al
it
y
sta
nd
a
r
d
[
3]
,
[
4]
that
are
c
on
si
der
e
d
to
be
te
chnolo
gical
bo
on
f
or
ever
y pro
j
ect
de
velo
pm
ent
te
a
m
.
On
e
of
the
t
echn
i
qu
e
s
to
e
ns
ure
a
n
e
ff
ect
ive
softwa
re d
e
velo
pm
ent
pr
a
ct
ic
es
is
to
ensu
re
hi
gh
e
r
de
gr
ee
of
risk
co
ntr
ol
m
e
asur
e
s
that
cal
l
s
fo
r
a
n
eff
ect
i
ve
risk
m
anag
e
m
ent
[5
]
.
Ba
si
cal
ly
,
a
risk
m
anag
em
ent
in
so
ftwa
re
de
velo
pm
ent
ind
us
try
is
al
l
about
consi
de
rin
g
al
l
so
rts
of
possible
fact
or
s
that
cou
l
d
de
grade
the
pro
duct
qu
al
it
y
or
inv
it
e
s
om
e
un
f
o
rt
un
at
e
c
ha
ll
eng
es
in
ne
ar
f
uture
du
ring
t
he
dev
el
op
m
ent peri
od that c
ould
po
s
sible c
os
t
so
m
e tang
ible
resou
rces
[6
]
,
[
7].
Ther
e
are
v
ario
us
li
te
ratu
res
e
.
g.
[8]
,
[
9] w
hic
h
sta
te
s
t
hat
va
rio
us
sta
nd
a
r
d
risk
m
anag
em
ent
m
od
el
s,
fr
am
ewo
r
ks
,
pract
ic
es,
et
c
ar
e
al
read
y
existi
ng
.
In
s
pite
of
this,
there
is
al
ways
an
un
pr
e
ceden
te
d
fea
r
of
r
i
s
k
du
e
to
fo
ll
ow
ing
reas
on
e
.
g.
c
ha
ng
e
of
m
anag
em
ent,
po
li
cy
al
te
rn
at
ion
,
sk
il
l
ga
p,
em
plo
ye
e
at
t
riti
on
,
requirem
ent
vola
ti
lity
,
su
dden
ad
op
ti
on
of
unkn
own
te
c
hnology
et
c.
Ba
si
cal
ly
,
there
ar
e
var
i
ous
ty
pes
of
ri
s
k
factors
wh
e
re
so
m
e
are
qu
it
e
known
i.e.
de
te
rm
inist
ic
fo
rm
wh
il
e
so
m
e
of
ab
so
l
utely
un
kn
own.
I
n
th
e
first
ty
pe
of
ris
k,
t
he
te
a
m
is
com
plete
ly
awar
e
of
the
ris
k
a
nd
ha
s
al
l
the
ch
an
ces
to
e
nsure
a
n
ef
fecti
ve
c
on
trol
o
f
the
risk.
Howe
ver,
sti
ll
var
iou
s
facto
rs
e.
g.
tim
e
inv
ol
ved
in
con
t
ro
ll
in
g
the
risk,
res
our
ce
inv
ol
ve
d,
c
os
t
et
c
are s
om
e factor
s that a
re als
o t
o
be
consi
der
e
d wh
il
e m
it
igatin
g suc
h form
s
of r
is
k.
Unfortu
natel
y,
the
sec
ond
f
orm
of
ris
k
fact
or
is
so
m
et
h
ing
that
t
he
s
of
t
war
e
de
velo
pm
ent
te
a
m
as
well
as
sta
keholde
r
has
a
bsolutel
y
no
pr
e
-
de
fine
d
in
form
at
i
on
or
cl
ue
a
bout
the
f
or
m
of
the
ris
k.
It
is
a
direct
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2088
-
8708
In
t J
Elec
&
C
om
p
En
g,
V
ol.
8
, N
o.
6
,
Dece
m
ber
2018
:
4735
-
474
4
4736
ind
ic
at
io
n
of
i
nvolv
em
ent
of
un
c
ertai
nty
fa
ct
or
in
t
he
ris
k
m
anag
em
ent
.
The
re
a
re
va
rio
us
stu
dies
wh
e
r
e
un
ce
rtai
nty
prob
le
m
s
[1
0]
,
[11]
ha
ve
bee
n
disc
us
se
d
a
s
the
m
os
t
chall
eng
in
g
pr
oblem
with
respec
t
to
com
pu
ta
ti
on
al
m
od
el
.
It
is
becau
se
s
uch
for
m
s
of
pro
blem
s
cannot
be
m
at
hem
a
ti
cal
l
y
fo
rm
ulate
d
as
var
ia
ble
s
corres
pondin
g
to
the
pro
blem
s
can
not
be
di
scre
te
ly
def
i
ne
d.
He
nce,
a
n
e
ff
ect
ive
risk
m
anag
em
ent
act
ually
su
f
fer
s
from
su
ch
f
or
m
s
of
prob
le
m
s
wh
ere
there
is
abs
olu
t
el
y
no
ben
c
hma
rk
e
d
m
od
el
or
so
luti
on
in
order
to
assist
s su
c
h
c
om
pu
ta
ti
on
al
ly
ch
al
le
ng
i
ng
prob
le
m
s.
On
e
way
to
dev
el
op
su
c
h
m
od
el
will
be
to
ta
ke
a
ca
se
stu
dy
an
d
def
i
ne
va
rio
us
co
ns
trai
nts
appr
opriat
e
to
case
stu
dy
an
d
pe
rfor
m
diff
e
r
ent
f
or
m
s
of
it
erati
on
s
to
che
ck
how
t
he
m
od
el
be
haves
i
n
risk
evaluati
on.
He
nce,
a
doptio
n
of
dif
fer
e
nt
op
tim
iz
at
ion
al
gorithm
s
[1
2]
,
[
13]
are
hi
gh
ly
r
ecom
m
end
e
d
in
this
case
as
they
cou
ld
offe
r
a
go
od
bala
nce
betw
een
co
ns
trai
nt
sat
isfact
ion
as
well
as
m
ini
m
i
ze
the
occura
nc
es
of
risk.
H
oweve
r,
w
hile
doin
g
s
uch
de
sig
n
a
pp
ro
ac
h,
it
cal
ls
f
or
va
rio
us
r
ound
s
of
it
erati
on
wh
e
re
so
m
e
rou
nd
s
of
it
erati
ons
c
ou
l
d
be
s
uffi
ci
ently
big
just
in
orde
r
to
ob
ta
in
an
el
it
e
ou
tc
om
e.
Su
ch
appr
oach
es
m
ay
be
com
plete
ly
un
pr
act
ic
al
eve
n
if
they
offe
r
good
outc
om
es.
Ther
e
f
or
e,
ther
e
is
a
nee
d
to
de
sign
a
nd
dev
e
lop
a
n
eff
ic
ie
nt
c
om
pu
ta
ti
on
al
m
od
e
l
that
is
ca
pab
l
e
of
c
on
t
ro
ll
in
g
the
ris
k
facto
r
to
a
good
e
xt
ent
that
is
ap
pl
ic
able
in
pract
ic
al
li
fe
[
14
]
-
[16].
This
re
searc
h
pap
e
r
i
ntrod
uc
es
one
s
uc
h
ded
ic
at
e
d
at
te
m
pt
wh
ere
a
si
m
ple
and
co
st
eff
ect
iv
e
m
od
el
ing
is
carried
ou
t usin
g
analy
ti
cal
m
eth
od
ology
in
order
to
c
om
pu
te
the
pr
act
ic
al
fo
rm
of
risk
as w
el
l
as
to
ensure
the
a
ccur
acy
in
it
.
The
seco
ndary
r
esearch
obj
ect
ive
is
to
of
fe
r
r
el
ia
ble
ou
tc
om
e
of
risk
c
om
pu
ta
ti
on
with
an
ai
d
of
m
achine
le
ar
ni
ng
a
ppr
oach.
The
te
rtia
ry
re
search
obje
ct
iv
e
of
t
his
pa
per
is
to
ens
ur
e
a
good
com
pu
ta
ti
on
al
m
od
el
that
cou
ld
ta
ke
the
rea
l
-
tim
e
con
strai
ns
ts
as
input
and
offer
reli
ab
le
risk
evaluati
on
t
o
assist
s
sta
keho
lders
for
f
or
m
ulati
ng
c
ounte
rm
easur
es.
Se
ct
ion
1.1
disc
us
ses
a
bout
th
e
existi
ng
li
te
r
at
ur
es
towa
rd
s
risk
m
anag
em
ent
fo
ll
ow
e
d
by
disc
ussi
on
of
resea
r
ch
pr
ob
le
m
s
in
Sect
ion
1.2
a
nd
pr
opos
e
d
s
olu
ti
on
in
1.3
.
Sect
io
n
2
discuss
e
s
about
al
go
rith
m
i
m
ple
m
entat
ion
f
ollo
we
d
by
disc
us
sio
n
of
res
ult
analy
sis
in
Sect
ion
3. Fina
ll
y, the concl
usi
ve
rem
ark
s a
r
e pro
vid
e
d
i
n
S
ect
ion
4.
1.1.
B
ackgr
ound
This secti
on
di
scusses
a
bout the ex
ist
in
g
li
te
ratur
e
s stu
dies tow
a
rd
s
risk
m
anag
em
ent as an
ex
te
ns
io
n
to
our
pr
io
r
re
view
work
[17
]
.
Discussi
on
t
ow
a
r
ds
im
po
rtance
of
ris
k
as
crit
ic
al
syst
e
m
was
pu
t
f
orward
by
Laplante
an
d
DeFranc
o
[
18
]
,
Lutz
an
d
H
ua
ng
[19]
.
A
co
m
par
at
ive
illustrati
on
of
dife
r
ent
risk
f
ram
e
works
hav
e
bee
n
dis
cusse
d
by
Pas
ha
et
al
.
[
20
]
.
It
has
bee
n
s
een
that
risk
m
anag
em
ent
of
fe
rs
com
plem
entart
ben
e
fits w
hile
exer
ci
se
d
on e
xisti
ng quali
ty
stand
a
rds.
The
stu
dy
of
Alba
darne
h
et
al.
has
discuss
ed
a
case
study
of
agile
m
eth
od
ology
with
resp
ect
to
it
s
sign
ific
a
nt
benefit
s
[21]
.
S
un
dar
a
raj
a
n
et
al.
hav
e
in
vestiga
te
d
towards
th
e
la
rg
e
scal
e
of
pr
oject
de
velo
pm
ent
for
assessi
ng
the
risk
ass
ocia
te
d
with
it
[22]
.
Lit
eratur
es
ha
ve
al
so
witness
ed
i
m
plica
ti
on
s
of
decisi
on
m
aking
towa
rd
s
risk
m
anag
em
ent
a
s
seen
in
wor
k
of
Aslam
et
al.
[
23
]
.
Im
po
rta
nce
of
sim
il
ar
pr
act
ic
es
of
ris
k
analy
sis
towa
r
ds
agile
m
et
ho
dolo
gy
was
al
so
f
ound
suppo
rted
in
t
he
work
of
Elba
nn
a
a
nd
Sar
ke
r
[
24
]
.
Ba
tbay
ar
et
al.
[25]
ha
ve
pr
e
sented
a
st
udy
wh
ere
sta
ti
sti
cal
too
l
has
use
d
f
or
as
sessi
ng
risk
al
on
g
with
app
ly
in
g fu
zzy
log
ic
.
T
he
stu
dies also
sho
w
s that ris
k
ass
oc
ia
te
d
with t
he sof
t
war
e
desi
gn
patte
rn
s
can
be
al
s
o
assessed
f
or
th
ei
r
risk
facto
r
us
in
g
sp
eci
fic
bound
a
ppr
oac
h
as
rep
or
te
d
by
Be
rn
ar
di
et
al.
[
26
]
.
A
dopt
ion
of
m
od
el
ing
ap
proach
f
or
us
i
ng
so
ci
al
an
d
te
ch
nical
syst
e
m
has
bee
n
prov
e
n
to
im
pr
ov
e
t
he
so
ft
war
e
desi
gn
a
s
well
as
offe
r
be
tt
er
risk
m
anag
em
ent
as
repor
te
d
by
Bi
der
an
d
Otto
[
27]
.
Sim
il
ar
fo
rm
of
resea
rch
w
ork
ha
s
been al
so re
por
te
d
by C
ha
dli
et
a
l.
[28].
Lit
eratur
es
have
al
so
intr
oduc
ed
integ
rated
-
ba
sed
ap
proac
h
wh
e
re
joint
im
plem
entat
ion
of
dif
fer
e
nt
form
s
of
discr
et
e
pr
oces
s
is
fou
nd
to
assist
s
in
m
ini
m
iz
ing
occ
ur
a
nces
of
ris
k.
T
his
f
act
was
discusse
d
by
Jan
j
ua
et
al.
[
29
]
.
Wor
k
of
Kendall
et
al.
[30]
hav
e
i
ntrod
uced
a
pr
a
ct
ic
al
centric
m
et
ho
dolo
gy
f
or
a
n
eff
ect
ive
gove
r
nan
ce
of
ris
k
f
act
or
.
Lue
dde
m
ann
et
al.
[
31]
ha
ve
prese
nt
ed
an
e
xperim
ental
-
base
d
a
ppr
oac
h
for
assessi
ng
sta
nd
a
rd
ISO
risk
ass
ociat
ed
with
cl
inic
al
dev
ic
es
.
Piet
ra
ntuono
et
al.
[32]
ha
ve
pres
ented
arch
it
ect
ure f
or
en
s
ur
i
ng ef
fec
ti
ve
softwa
re rel
ia
bili
ty
.
Ado
ption
of
prob
a
bili
sti
c
-
bas
ed
a
ppr
oach
w
as
re
ported
to
m
ini
m
iz
e
the
c
om
plexiy
asso
ci
at
ed
wit
h
evaluati
on
of
r
isk
associat
e
d
with
m
edical
dev
ic
e
s.
T
his
fa
ct
was
furthe
r
fou
nd
a
dvocat
ed
in
the
wor
k
of
Ra
o
et
al.
[33]
,
[
34]
that
incli
nes
towards
ide
ntific
at
ion
of
risk
facto
r.
St
ud
y
to
wards
ta
xonom
ie
s
of
risk
m
anag
em
ent
was
disc
us
se
d
by
Re
ye
s
et
al
[3
5].
A
doptio
n
of
e
vo
l
ution
te
chnqiues
are
al
so
repo
rte
d
to
offe
r
bette
r
m
anag
e
m
ent
and
co
ntr
ol
of
ris
k
duri
ng
the
s
of
twa
r
e
pr
oject
de
velop
m
ent.
This
f
act
was
discusse
d
by
Sarro
et
al.
usi
ng
m
ulti
-
obj
e
ct
ive
base
d
a
ppr
oac
h
in
orde
r
to
inc
orporat
e
ada
ptivit
y
in
their
desig
n
[
36
]
.
Usage
of
s
of
t
war
e
i
ntell
igen
c
e
was
in
vestigat
ed
by
Susar
ev
et
al.
i
n
or
der
t
o
stu
dy
about
the
sa
fety
factors
essenti
al
for
e
ssentia
l
so
ft
w
are
m
anag
em
e
nt
[37]
.
T
her
e
exists
var
i
ous
form
s
of
re
search
-
based
s
tud
ie
s
towa
rd
s
risk
m
anag
em
ent
but
ver
y
le
ss
m
odel
s
are
f
ound
t
o
be
in
vestigat
ed
f
ro
m
the
vi
ewpoint
of
sof
tware
eng
i
neer
i
ng.
More
over,
ver
y
le
ss
com
pu
ta
ti
on
al
m
od
el
with
sign
ific
ant
be
nch
m
ark
in
g
ha
s
been
re
porte
d.
The
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
Elec
&
C
om
p
En
g
IS
S
N: 20
88
-
8708
PORM
:
Pre
dic
ti
ve Op
ti
miz
ation of
Risk
M
anag
e
me
nt to
...
(
Sa
l
ma Fird
os
e)
4737
nex
t
sect
io
n
outl
ines
the
res
earch
prob
le
m
s
that
are
arr
iv
ed
from
rev
ie
wing
the
existi
ng
a
ppr
oach
t
ow
a
r
ds
so
ft
war
e
r
is
k m
anag
em
ent.
1.2.
Th
e Pr
ob
l
em
The
si
gn
i
ficant
r
esea
rch p
robl
e
m
s ar
e as
fo
ll
ow
s:
a.
Existi
ng
resea
rch
w
ork
a
re
m
or
e
theo
reti
cal
/
con
ce
ptu
al
iz
ed
m
od
el
an
d
la
cks
co
m
pu
ta
ti
on
al
m
od
el
ing
as
pe
ct
w
it
h resp
ect
to an
al
yt
ic
al
sol
ution
a
ppr
oac
h.
b.
Unde
rtakin
g
of
real
-
t
im
e
risk
facto
r
an
d
f
or
m
ulati
ng
it
i
n
analy
ti
cal
m
od
el
in
g
is
le
ss
witnessed
in
existi
ng appr
oa
ch
that
re
du
ce
s
the a
pp
li
ca
bili
ty
o
f
cl
ai
m
ed
su
ccess
of m
odel
.
c.
Ther
e
is
no
m
ajor
op
ti
m
iz
ati
on
te
ch
nq
i
ues
i
m
ple
m
ented
over
softwa
re
e
ng
i
neer
i
ng
wit
h
m
any
of
them
a
re f
ound
not to
f
ocu
s
on red
ucin
g
t
he i
te
rati
on
.
d.
More
incli
nation
to
wards
m
od
el
in
g
as
pect
and
le
ss
towa
rd
s
e
xp
l
or
i
ng
the
app
li
cabi
li
ty
of
th
e
m
od
el
ing
in
r
e
al
-
tim
e scenar
io.
Ther
e
f
or
e,
the
pro
blem
state
m
ent
of
t
he
propose
d
st
ud
y
c
an
be
sta
te
d
a
s
“
Develo
ping
a
cost
ef
fe
ct
iv
e
comp
uta
ti
on mod
el
to off
er a
pr
eci
se eval
ua
t
ion
of the critical risk factor
for the
give
n
s
et
o
f op
er
atio
nal d
ata
wi
th
rel
iab
le
pr
e
dicti
ve
performance
is
quit
e
cha
ll
en
gi
ng
t
as
k
”.
T
he
nex
t
sect
io
n
discuss
e
s
ab
ou
t
the
pro
po
se
d
m
et
ho
dolo
gy
us
e
d
t
o
c
ounterm
easur
e
the a
bove
s
ta
te
d
resea
rch
pro
blem
.
1.3.
Th
e Pr
oposed
So
lu
tio
n
The
pro
posed
work
is
basical
ly
an
exte
ns
io
n
of
t
he
our
pr
ior
desig
n
a
ppr
oach
cal
le
d
as
3LRM
[
38]
wh
e
re
the
pre
sent
w
ork
f
oc
us
es
on
opti
m
iz
ing
the
pe
rfor
m
ance
of
th
e
com
pu
ti
ng
r
isk
fact
or
i
nvol
ved
i
n
so
ft
war
e
engin
eerin
g.
T
he a
rc
hitec
ture o
f
the
prop
os
ed
P
ORM i
s shown i
n Fi
gure
1
.
Figure
1
.
Pro
pose
d
A
rc
hitec
ture
of PORM
The
im
ple
m
e
ntati
on
of
th
e
propose
d
syst
e
m
is
carriedout
consi
de
rin
g
analy
ti
cal
researc
h
m
et
ho
dolo
gy
wh
e
re
the
em
ph
a
sis
is
ren
de
red
on
de
vel
op
i
ng
the
in
puts
associat
ed
with
the
rea
l
-
tim
e
dev
el
op
m
ent
scenari
o
in
a
ny
IT
organ
iz
at
io
n
wit
h
res
pect
to
it
s
software
pro
j
ect
s.
T
he
pro
posed
syst
em
ta
kes
four
dif
fer
e
nt
f
or
m
s
of
i
nputs
e.g.
i)
devel
op
m
en
t
exp
e
ndit
ur
es
pe
r
s
of
t
w
are
pr
oj
ect
s
(r
1
),
ii
)
total
nu
m
ber
of
al
locat
ed
s
of
t
war
e
pro
j
ect
s
per
em
plo
ye
e
(r
2
)
,
ii
i)
al
loca
te
d
de
velo
pm
ent
dur
at
ion
f
or
each
pro
j
ect
s
on
a
n
e
m
plo
ye
es
(
r
3
)
,
an
d
i
v)
unce
r
ta
inty
factor
(
r
4
).
T
he
first
t
hree
inputs
(i.e.
r
1
,
r
2
,
a
nd
r
3
)
c
an
be
ca
ptured
from
any real
-
ti
m
e stat
ist
ic
s o
f
an
org
a
nizat
ion w
hi
le
the fourth
input .i.e
. r
4
is r
andom
ly
init
ial
iz
ed.
The
pro
posed
syst
e
m
app
li
e
s
neural
network
a
nd
e
xpli
ci
te
ly
us
es
two
diff
e
re
nt
no
n
-
li
nea
r
op
ti
m
iz
ation
f
un
ct
io
ns
wh
il
e
perform
i
ng
trai
nin
g
over
it
s
hidden
la
ye
r
in
orde
r
to
ob
ta
in
bette
r
f
orm
of
ou
tc
om
e
associat
ed
with
risk.
Af
te
r
the
trai
ning
pr
oce
ss
is
accom
plished,
the
pro
po
s
ed
syst
e
m
offe
r
s
nu
m
erical
evaluati
on
of
t
he
ri
sk
fact
or
ass
oc
ia
te
d
with
the
case
stud
y
of
a
n
orga
nizat
i
on
giv
e
n.
T
his
out
com
e
is
of
hi
gh
e
r
i
m
po
rtance
f
or
any
sta
ke
holders
of
a
n
org
anizat
ion
so
t
hat
they
ca
n
f
or
m
ulate
an
e
ff
ect
ive
decisi
on
f
or
re
sist
ing
an
up
c
om
ing
risk
e
vent
.
The
un
der
ta
ki
ng
of
de
ci
sion
is
fu
rthe
r
co
nc
reti
zed
by
chec
k
t
he
accuracy
val
ue
of
the
pro
po
s
e
d
pr
e
dicti
on
operati
on
f
ollo
we
d
by
conve
rg
e
nce
te
st
fo
r
an
eff
ect
ive
valid
at
ion
.
The
c
om
plete
stud
y
outc
om
e
is
assesse
d
wi
th
re
sp
ect
t
o
t
he
pe
rfor
m
ance
par
am
et
ers
tha
t
are
sta
nd
a
r
dized
i
n
the
area
of
soft
war
e
e
ng
i
neer
i
ng
a
nd
sti
ll
us
ed
in
the
in
dustr
y
in
or
de
r
to
as
sess
the
softwa
re
qual
it
y.
The
nex
t
sect
ion
il
lustrat
es ab
out t
he
al
gorithm
i
m
ple
m
entat
ion
fo
r
t
he
a
bove disc
usse
d
m
et
ho
dol
og
y.
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2088
-
8708
In
t J
Elec
&
C
om
p
En
g,
V
ol.
8
, N
o.
6
,
Dece
m
ber
2018
:
4735
-
474
4
4738
2.
ALGO
RITH
M
I
MPLEME
NTATIO
N
The
pri
m
e
pu
r
po
s
e
of
this
al
gorithm
is
to
design
a
nd
de
ve
lop
a
n
al
gorith
m
that
bear
s
th
e
capab
il
it
y
for
pr
eci
sel
y
c
om
pu
ti
ng
ris
k
for
a
gi
ven
sta
te
of
in
form
at
i
on
ass
ociat
ed
with
t
he
m
et
ho
dolo
gy
a
dopt
ed
for
so
ft
war
e
pr
oj
e
ct
dev
el
opm
ent
cy
cl
e.
The
alg
ori
thm
con
str
uction
is
basic
al
ly
carried
out
in
two
ph
ase
s
wh
ere
the
first
ph
as
e
con
ce
ntrates
i
n
com
pu
ta
ti
on
of
risk
facto
r
and
seco
nd
ph
ase
co
ncen
t
rates
on
com
pu
ti
ng
t
he
a
m
ou
nt
of
reli
abili
ty
scor
e
associat
ed
with
the
c
om
pu
ta
ti
on
.
T
he
pro
po
s
ed
P
ORM
syst
e
m
al
so
a
pp
li
es
m
achine
le
arn
i
ng
in o
r
der
to carr
y
out
pr
e
di
ct
ive
op
ti
m
iz
a
t
ion
ope
rati
on.
The
al
gorithm
ta
kes
in
the
input
of
r
(r
is
k
assessm
e
nt
data)
that
a
f
te
r
processi
ng
yi
el
ds
R
deg
(D
egr
ee
of
Ri
sk).
The
ste
ps
of
t
he
propose
d
al
gorithm
are as
f
ollow
s:
Algori
th
m
f
or
Compu
tin
g R
isk Fac
t
or
Inpu
t
:
r
(r
is
k
a
ssessm
ent d
at
a)
Out
p
ut
: R
deg
(
Degree
of Ri
sk)
St
ar
t
1.
i
nit
r
2.
α
f
1
(r
in
), β
f
1
(r
out
)
3.
For i=
1:n
1
4.
c
i
i
i
i
r
w
r
s
in
in
)
(
)
(
)
(
)
(
.
1
5.
En
d
6.
For i=
1:n
2
7.
c
i
i
i
i
r
w
r
s
out
out
)
(
)
(
)
(
)
(
.
1
8.
En
d
9.
For i=
1:n
3
10.
)
(
))]
(
)
(
.(
)
(
[
1
1
2
i
i
i
w
c
i
T
T
out
11.E
nd
12. r
deg
f
1
(r
out
-
T
out
)
End
The
descr
i
ptio
n
of
the
a
bove
al
gorithm
is
as
fo
ll
ows:
The
in
put
of
this
al
gorithm
is
basical
ly
represe
nted
as
r
(r
isk
assessm
ent
data)
w
hic
h
is
con
si
der
e
d
to
be
co
ns
ist
ing
of
i)
de
velop
m
ent
exp
e
ndit
u
res
per
s
oft
wa
re
pro
j
ect
s
(
r
1
)
,
ii
)
total
nu
m
ber
of
al
locat
e
d
s
of
t
war
e
pro
j
ec
ts
per
em
plo
ye
e
(r
2
),
ii
i)
al
locat
e
d
dev
el
op
m
ent
durati
on
f
or
eac
h
pro
j
ect
s
on
an
em
plo
ye
es
(r
3
)
,
an
d
iv
)
uncertai
nty
fact
or
(
r
4
)
.
A
data
in
the
form
of
.csv
fi
le
is
colle
ct
ed
from
a
case
st
ud
y
a
nd
is
co
nsi
der
e
d
as
an
i
nput
(Li
ne
-
1).
The
ne
xt
pa
rt
of
the
i
m
ple
m
entat
io
n
is
associat
e
d
with
proces
sing
the
i
nput
s
correct
ly
.
Fo
r
this
pur
po
s
e,
a
functi
on
f
1
(x
)
is
const
ru
ct
e
d
t
ha
t
extract
t
he
m
ini
m
u
m
and
m
axi
m
u
m
valu
e
of
it
s
in
put
a
rgum
ents
r
in
a
nd
r
out
t
hat
re
presents
the
input
and
ou
t
pu
t
ar
gum
e
nts
res
pecti
vely
(Line
-
2)
.
A
look
is
con
st
ruct
ed
for
al
l
the
values
of
th
e
inp
ut
argum
ents
n
1
(Line
-
3)
a
nd
th
eref
or
e
the
value
of
n1
is
4
i.e.
r={
r
1,
r
2,
r
3,
r
4
}.
It
sh
ould
be
under
st
ood
th
at
r
is
a
raw
data
an
d
he
nce,it
is
essen
ti
al
to
carry
out
no
rm
al
iz
at
ion
in
orde
r
to
eas
e
dow
n
the
c
om
pu
ta
ti
on
proc
ess
of
op
ti
m
iz
ation
.
The
pro
posed
al
gorithm
co
m
pu
te
s
the
norm
al
iz
ed
input
ri
n
(Line
-
4)
us
in
g
a
sim
plifie
d
e
m
pirical
ex
pr
ession as
fo
ll
ows:
c
i
i
i
i
r
w
r
s
in
in
)
(
)
(
)
(
)
(
.
1
(1)
In
the
ab
ove
expressi
on,
the
com
pu
ta
ti
on
of
the
norm
al
ized
input
associ
at
ed
with
the
r
isk
factor
is
com
pu
te
d.
Th
e
de
penda
ble
var
ia
bles
use
d
in
the
e
xpres
sion
are
w
(
w
ei
gh
t)
,
r
in
init
ia
l
risk
i
nput
e
lem
ent
corres
pondin
g
to
i
th
el
e
m
ent,
wh
e
re
i≤
n
1
,
α
(
pr
im
ary
inp
ut co
r
res
pondin
g
to
sam
e
i
th
e
leme
nt
of
h
ig
her
/
lowe
r
order
i.e
.
α
2
and
α
1
res
pecti
ve
ly
),
an
d
a
sta
ti
sti
cal
con
sta
nt
c
.
Alm
os
t
si
m
i
la
r
m
echani
s
m
is
con
ti
nued
f
or
com
pu
ti
ng
t
he norm
al
iz
ed
ou
t
pu
t
(ro
ut) usin
g
the
foll
owin
g ex
pr
es
sio
n
c
i
i
i
i
r
w
r
s
out
out
)
(
)
(
)
(
)
(
.
1
(2)
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
Elec
&
C
om
p
En
g
IS
S
N: 20
88
-
8708
PORM
:
Pre
dic
ti
ve Op
ti
miz
ation of
Risk
M
anag
e
me
nt to
...
(
Sa
l
ma Fird
os
e)
4739
The
pro
pose
d
stud
y
co
ns
id
er
s
that
there
ar
e
two
outc
om
es
of
stu
dy
i.e
.
i)
Qu
a
ntize
d
Ri
sk
and
ii
)
Pr
e
dicti
ve
Acc
ur
acy
.
I
n
the
above
e
xpres
sion,
the
de
pe
ndable
va
riable
s
are
r
out
init
ia
l
risk
outp
ut
and
β
(prim
ary
ou
tp
ut
corres
pondin
g
to
sam
e
i
th
ele
m
ent
of
hi
ghe
r
/
lo
we
r
order
i.e.
β
2
an
d
β
1
r
especti
vely
).
It
al
so
us
es
sam
e
wei
gh
t
a
nd
sta
ti
sti
cal
const
ant
(L
ine
-
7)
.
T
he
a
bove
tw
o
em
pirical
expressio
n
sig
nificant
a
ss
ist
s
to
extract
the
nor
m
al
iz
ed
inp
ut
and
outp
ut
of
the
risk
facto
r
in
m
os
t
co
m
pu
ta
ti
onal
ly
eff
ic
ie
nt
m
ann
er
as
it
involves
faster
tim
e
of
extra
ct
ion
with
re
duci
ng
the
dep
e
nd
e
ncies
of
re
adin
g
m
assive
el
e
m
ents
of
the
ra
w
database
of
t
he
risk
m
anag
e
m
ent
wit
hin
a
n
orga
nizat
ion
.
This
proce
ss
is
a
ste
ppin
g
s
ton
e
of
op
ti
m
i
zat
ion
wh
e
re
with
out
inv
ol
ving
any
extra
res
ource
s,
the
pro
pose
d
syst
e
m
is
a
tte
m
pting
to
obt
ai
n
bette
r
num
erical
values
with
hig
he
r
de
gr
ee
of
accu
racy
a
nd
reli
abili
ty
.
The
la
st
phase
of
t
he
al
g
ori
thm
i
m
ple
m
entat
ion
agai
n
const
ru
ct
s
a
lo
op
with
it
erati
on
re
stric
te
d
to
n
3
that
c
orresponds
t
o
nu
m
ber
of o
utc
om
es
i
.e.
2
f
or
the p
r
opose
d
syst
e
m
. Th
e e
xpressi
on intr
od
uced f
or com
pu
ti
ng the test
outc
om
e o
f
t
he pr
opos
e
d
syst
e
m
is as f
ollow
s
:
)
(
))]
(
)
(
.(
)
(
[
1
1
2
i
i
i
w
c
i
T
T
out
(3)
Howe
ver,
it
is
interest
ing
t
o
underst
an
d
th
e
form
ulati
on
of
the
te
st
-
outc
om
e
T
out
(Li
ne
-
10)
that
finall
y
le
ads
to
app
ly
in
g
sim
i
l
ar
f
un
ct
i
on
f
1
(
x)
offer
t
he
dif
fer
e
nce
of
r
out
norm
al
iz
ed
ou
tpu
t
of
risk
with
T
ou
t
te
st
ou
tc
om
e
in
or
der
t
o
obta
in
r
deg
de
gree
of
risk
(Li
ne
-
12)
.
T
he
com
puta
ti
on
of
the
te
st
ou
tc
om
e
is
carried
ou
t
i
n
a
n
e
xpli
ci
t
m
ann
er w
it
h
in
volvem
ent
of
the n
e
ur
al
net
work
-
ba
sed
p
re
dicti
ve
op
e
r
at
ion
. Th
e
ste
ps
of
th
e
al
gorithm
are
as foll
ow
s:
Algori
th
m
for
Op
timi
z
at
ion
a
n
d
Co
m
pu
ting
Tes
t Outc
om
e
Inpu
t
: s
(size
of n
et
work),
r
i
n/r
ou
t
(no
rm
ali
zed inp
ut and
outp
ut of
risk fa
ct
or
)
Out
p
ut
:
T
out
(
validat
ed
test
outcom
e
St
ar
t
1.
i
nit s
2.
γ
f
1
(r
in
)
3.
u
τ(
γ,
s
, ω)
4.
op
f
2
(
u, r
in
,
r
out
)
5.
T
out
f
3
(
u,
r
in
)
End
As
the
al
gorithm
i
m
ple
m
ent
s
ne
ur
al
netw
ork,
it
is
esse
ntial
that
it
sh
ould
posses
an
ef
fecti
ve
dim
ension
of
it
s
netw
ork
(c
on
sist
ing
of
in
pu
t
,
hidde
n,
an
d
outp
ut
la
ye
r)
.
T
he
va
riable
s
r
epr
ese
nts
siz
e
of
the
netw
ork
f
ollo
wed
by
i
m
plem
entat
ion
of
sim
il
ar
fu
nctio
n
f
1
(x
)
c
onsideri
ng
t
he
input
ar
gu
m
ent
of
nor
m
al
iz
ed
input
i.e.
r
in
(Line
-
2).
The
pro
posed
syst
e
m
than
ap
pl
ie
s
a
fee
d
f
orwa
rd
bac
kpr
opagati
on
f
un
ct
ion
τ
consi
der
i
ng
th
e
inp
ut arg
um
ents o
f
sort ou
t n
or
m
al
iz
ed
inp
ut
v
al
ue
ob
ta
i
ne
d
from
Line
-
2,
siz
e o
f
t
he
net
work,
and
no
n
-
li
nea
r
functi
on
ω
.
T
he
ap
plica
ti
on
of
the
fee
d
f
orw
ard
f
un
ct
i
on
τ
assist
s
in
form
i
ng
a
netw
ork
that
is
trai
ned
in
one
-
directi
on
a
nd
i
s
com
plete
ly
i
nd
e
pe
nd
e
nt
on
f
or
m
at
ion
of
any
ki
nds
of
c
om
pu
ta
ti
on
al
l
oops
.
The
pro
pose
d
op
ti
m
iz
ation
is
al
so
carried
out
by
consi
der
i
ng
tw
o
no
n
-
li
ne
ar
optim
iz
ation
proces
s
i.e.
ta
ns
i
g
and
pureli
n.
T
he
ta
ns
i
g
is
ba
sic
al
ly
a
fo
rm
of
tra
nsfer
fun
ct
ion
that
is
re
sp
onsi
ble
for
c
om
pu
ti
ng
the
outp
ut
of
a
la
ye
r
fo
r
a
gi
ven
value
of
input
la
ye
r.
Th
e
nu
m
erical
ou
tc
om
e
of
this
f
un
ct
io
n
is
eq
uiv
al
ent
to
hype
rbolic
functi
on
of
ta
ngent.
T
he purel
in fu
nction i
s a
no
t
her f
or
m
o
f t
ran
s
fer
f
un
ct
io
n use
d
Fi
gure
2.
Figure
2
.
G
raphical
Repre
sen
ta
ti
on
of
purel
in
a
nd
ta
ns
ig
F
un
ct
io
ns
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2088
-
8708
In
t J
Elec
&
C
om
p
En
g,
V
ol.
8
, N
o.
6
,
Dece
m
ber
2018
:
4735
-
474
4
4740
The
al
gorithm
i
m
ple
m
ents
a
de
fine
d
trai
nin
g
pr
ocess
f
2
(
x)
co
ns
ide
rin
g
the
in
put
ar
gum
ents
of
netw
ork
u
an
d
norm
al
iz
ed
inp
ut/ou
t
pu
t
var
ia
bles
i.e.
r
in
/
r
out
(Line
-
4).
Fina
ll
y,
an
ag
gr
e
ga
te
functi
on
f
2(x)
f
or
si
m
ulati
ng
the
neural
net
work
m
od
el
is
im
ple
m
ent
ed
and
a
ppli
ed
over
the
trai
ning
netw
ork
i.e
u
a
nd
norm
al
iz
ed
inp
ut
value
of
the
risk
facto
r
rin
(Line
-
5).
T
his
nu
m
erical
assessm
ent
of
this
m
od
el
is
carrie
d
out
by
cal
culat
ing
the
T
out
obta
in
ed
by
co
ns
ide
r
ing
t
he
net
wor
k
gen
e
rated
c
onside
rin
g
the
ri
n
(
w
he
re
r
in
=r
1
,
r
2
,
r
3
,
and
r
4
)
well
-
de
fine
d
by
num
erical
at
tribu
te
s
of
it
s
el
em
e
nts.
T
he
refor
e
,
the
propose
d
optim
iz
at
ion
m
od
el
offer
s
a
si
gn
i
f
ic
ant
inf
orm
ation
a
bout
the
risk
fact
or
s
consi
der
i
ng
th
e
ne
r
-
real
w
or
ld
num
erical
values
associat
ed
wit
h
4
var
ia
bles
of
ris
k.
H
ow
e
ver,
the
la
st
ri
sk
var
ia
ble
i.e.
r
4
is
c
onside
r
ed
as
1
that
is
hi
gh
es
t
value
of
prob
a
bili
ty
to
perfor
m
an
inv
est
iga
ti
on
of
it
s
i
m
pact
on
ris
k
a
nlay
sis.
The
ne
xt
sect
ion
outl
ines
th
e
resu
lt
s
obta
ine
d by im
ple
m
en
ti
ng
the
pr
opose
d pr
e
dicti
ve o
pti
m
iz
at
ion
techn
i
qu
e
.
3.
RESU
LT
A
N
ALYSIS
The
im
ple
m
entat
ion
of
the
pro
po
se
d
PO
R
M
syst
e
m
is
carried
out
usi
ng
M
ATL
AB
w
her
e
t
he
al
gorithm
i
m
pl
e
m
ented
in
pri
or
sect
ion
ha
s
been
exec
ute
d
unde
r
var
io
us
te
st
-
cases
ass
oc
ia
te
d
with
i
niti
al
iz
ed
value
of
ris
k
fa
ct
or
r
.
T
he
pro
po
s
ed
im
ple
m
e
nt
pro
gram
m
atical
ly
con
trols
the
di
ff
e
ren
t
va
lue
of
r
1
,
r
2
,
r
3
,
a
nd
r
4
f
ollo
wed b
y
us
a
ge of ne
ur
al
n
et
w
ork
to
olbox f
or assessi
ng the
outcom
e o
f
the
pr
e
dicti
ve
optim
iz
at
ion
.
The
com
plete
assessm
ent
of
the
stud
y
outc
om
e
is
carried
ou
t
co
ns
ide
ring
the
nu
m
erical
values
as
sh
ow
n
in
Tabl
e
1.
T
he
PO
R
M
syst
e
m
ta
k
es
the
4
dif
fere
nt
ty
pes
of
i
nput
of
risk
r
in
i.e.
i)
de
velo
pm
ent
exp
e
ndit
ur
es
pe
r
softwa
re
pr
oj
ect
s
(r
1
)
,
ii
)
total
nu
m
ber
of
al
locat
ed
software
pro
j
ect
s
per
em
plo
ye
e
(
r
2
),
ii
i)
al
lo
cat
ed
devel
op
m
ent
durati
on
f
or
each
pr
oj
ect
s
on
an
e
m
plo
ye
es
(r
3
),
and
iv
)
uncerta
inty
factor
(r
4
).
Af
te
r
app
ly
in
g
funct
ion
f
1
(
x),
the
num
erical
ou
tc
om
es
are
sho
wn
as
m
ini
m
u
m
a
nd
m
axi
m
u
m
values
i
n
3
rd
a
nd
4
th
colum
n
of
the
above
ta
ble.
The
pro
pose
d
syst
e
m
al
so
us
es
a
scal
ing
factor
f
or
fur
ther
norm
al
iz
i
ng
the
nu
m
eric for
be
tt
er conv
e
rgen
ce o
utcom
e.
Figure
3
sho
w
s
the
grap
hical
ou
tc
om
e
of
th
e
conve
rg
e
nce
te
st,
wh
e
re
it
can
be
obser
ve
d
that
curve
for
trai
nn
g
(
ob
ta
ined
f
r
om
nu
m
erical
ou
tc
om
e
of
P
ORM)
as
well
as
be
st
fit
j
ust
overla
ps
each
oth
e
r
s
howi
ng
a
good
ag
gr
em
ent
wit
h
the
pr
opos
e
d
syst
em
.
It
sho
ws
that
por
posed
m
ec
han
ism
of
com
pu
ti
ng
ris
k
fa
c
tor
is
highly
reli
able
m
at
he
m
at
ic
a
lly
fo
r
a
giv
e
n
s
cenari
o
of
c
onstrai
nts
(i.e.
r
in
).
The
best
par
t
of
the
stud
y
o
utcom
e
is
that
it
is
co
m
ple
te
ly
fr
ee
f
ro
m
higher
ra
nges
of
it
erati
ve
operati
on
as
it
just
need3
-
4
i
te
rati
on
s
i
n
order
to
ob
ta
in
reli
a
ble
ou
tc
om
e
of
ris
k
facto
r.
A
n
i
nt
eresti
ng
fact
or
to
obser
ve
f
rom
Figu
re3(a)
a
nd
Fig
ure3
(
b)
is
that
the
propose
d
s
yst
e
m
m
in
i
m
izes
the
final
ep
och
of
40
11
to
2178
out
of
total
of
20
,
000
e
po
c
h,
wh
ic
h
di
rectl
y
m
eans
that
irresp
ect
ive
of
an
y
total
nu
m
ber
of
init
ia
li
zed
epo
c
h,
the
pro
po
s
ed
syst
em
perform
s
op
ti
m
iz
at
ion
at
the
cost
of
extrem
el
y
red
uce
d
num
ber
of
ep
oc
h
the
reb
y
e
xh
i
bit
in
g
highly
redu
ced
dep
e
nden
ci
es
of
com
pu
ta
ti
on
al
resou
rces.
Table
1
S
umm
ary o
f Nu
m
erical
V
al
ues
of P
a
ram
et
ers
of
P
O
RM
Para
m
eters
Para
m
eter
Min
-
Valu
e
Max
-
Valu
e
Scale Fa
cto
r
r
in
r
1
0
.3
0
.5
0
.7
r
2
2
3
4
r
3
1
3
3
r
4
1
4
4
r
o
u
t
r
o
u
t1
0
.6
0
.8
1
.3
r
o
u
t2
4
.9
1
3
.8
1
8
.7
The
stu
dy
outc
om
e
of
the
pro
po
s
ed
PR
OM
syst
e
m
has
been
com
par
ed
with
the
existi
ng
s
yst
e
m
.
The
proce
dure
ad
opte
d
a
re:
-
the
hypothesis
of
the
pro
posed
syst
e
m
con
cern
i
ng
a
bout
risk
factor
is
truell
y
associat
ed
wit
h
the
s
of
t
war
e
en
gin
ee
rin
g
fiel
d
w
he
re
the
r
e
are
m
ulti
ple
perform
ance
pa
ram
eers
requi
red
to
assess
any
s
oft
war
e
de
sig
n.
The
pr
opos
e
d
syst
e
m
con
sid
ers
5
fr
e
qu
e
ntl
y
adopted
s
of
t
war
e
par
am
at
e
rs
e.
g.
PF
-
1
(
W
ei
gh
te
d
Me
th
od
pe
r
Cl
ass),
P
F
-
2(
R
esp
on
se
f
or
Cl
asses),
PF
-
3
(
Dep
t
h
o
f
I
nh
e
rita
nce
T
ree),
PF
-
4
(Couplin
g
Be
t
ween
O
bject
s),
an
d
PF
-
5
(
N
um
ber
of
C
hilde
rn).
T
hese
are
t
he
sta
nda
rd
m
e
tric
that
co
ntr
ol
s
the
qu
al
it
y
of
s
oft
war
e
desig
n
a
nd
de
gr
a
datio
n
in
any
of
thes
e
qu
al
it
y
facto
rs
w
ould
cal
l
f
or
risk
facto
r.
Hen
ce
,
the
stu
dy
sel
ec
ts
the wor
k
ca
r
ried
out by
Z
hou
a
nd
Le
nug
[
39
]
a
nd p
er
f
orm
s
co
m
par
ison
with
each
o
the
r.
Th
e
ou
tc
om
es are disc
us
se
d
wit
h
r
espect t
o t
he p
erfor
m
ance m
e
tric
:
a.
An
alysis
of
PF
-
1
(
Wei
ghte
d
Met
hod
pe
r
Cl
as
s)
:
As
childr
en
will
inh
e
rit
m
axi
m
u
m
nu
m
ber
of
m
et
ho
ds
for
a
giv
e
n
cl
ass
and
gr
eat
e
r
value
of
it
will
act
as
an
i
m
ped
ie
m
ent
t
ow
a
r
ds
sin
gle
desig
n
us
a
ge
of
so
ft
war
e
desi
gn that m
ay
b
e o
bsole
te
in fut
ur
e
. H
e
nce,
lo
wer
value
of R
F
-
1 sh
own
b
y
PO
RM
s
hows
t
hat
pro
po
se
d
syst
e
m
o
ff
ers g
ood desig
n reu
se f
l
exibili
ty
.
Evaluation Warning : The document was created with Spire.PDF for Python.
In
t J
Elec
&
C
om
p
En
g
IS
S
N: 20
88
-
8708
PORM
:
Pre
dic
ti
ve Op
ti
miz
ation of
Risk
M
anag
e
me
nt to
...
(
Sa
l
ma Fird
os
e)
4741
b.
An
alysis
of
P
F
-
2
(
Res
pons
e
for
Cl
as
ses)
:
Higher
value
of
t
his
pe
rform
ance
m
e
tric
will
on
ly
in
vite
higher
c
om
pu
ta
ti
on
al
effor
t
of
debu
gg
i
ng
and
he
nce
res
ult
in
com
plexity
.
It
can
be
seen
that
P
ORM
offer
s
h
i
gh
ly
r
edu
ce
d
c
om
plexity
in
this ca
s
e.
c.
An
alysis
of
P
F
-
3
(
De
pth
of
I
nh
eri
ta
nce
T
ree)
:
Higher
va
lue
of
t
his
pe
rfor
m
ance
m
etr
ic
will
cal
l
f
or
higher
in
her
it
a
nce
m
aking
th
e
so
ftwa
re
dei
sg
n
m
or
e
unpr
edict
able
an
d
m
or
e
co
m
plex.
Hen
ce
,
lowe
r
value o
f
this
pe
rfor
m
ance m
etr
ic
on PR
OM s
hows bett
er
ou
tc
om
e.
d.
An
alysis
of
PF
-
4
(
Coupli
ng
Bet
we
en
Obje
ct
s
)
:
In
creas
ed
value
of
it
will
on
ly
m
ean
increase
m
ai
ntainance
wh
ic
h
is
abso
l
utely
no
t
cost
eff
ect
ive
.
Therefo
re,
pro
pose
d
PO
RM
offe
r
s
cost
eff
ect
iv
e
op
ti
m
iz
ation
outc
om
e.
e.
An
alysis
of
PF
-
5
(
Number
of
Chil
dern
)
:
In
cr
eased
val
ue
of
these
m
et
ric
ca
ll
s
fo
r
chall
en
ge
in
ob
ta
inin
g
ineff
ic
ie
nt
abst
racti
on
by
t
he
par
e
nt
cl
ass
t
ha
t
resu
lt
s
i
n
in
creasin
g
desig
n
c
om
plexity
.
Hen
ce
,
PR
OM
do
e
sn’t o
ff
e
r
a
ny such
co
m
plexity
.
Ap
a
rt
from
thi
s,
the
pro
posed
syst
e
m
con
s
um
es
0.
2335
4
seconds
wh
il
e
ex
ist
ing
syst
em
took
1.8
7762
s
econds
of
c
om
pu
ta
ti
on
al
tim
e.
Hen
c
e,
in
eve
ry
res
pect,
the
pr
opos
e
d
syst
e
m
c
an
be
sai
d
to
offer
bette
r
pr
edict
ive
perform
ance o
f
optim
iz
at
ion
in
s
of
t
war
e
eng
ineerin
g.
(a)
A
naly
sis F
or 40
11 Epoc
h
(b) Analy
sis F
or 21
78
Ep
oc
h
Figure
3
.
O
utc
om
e o
f
Co
nver
gen
ce
Analy
sis
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
S
N
:
2088
-
8708
In
t J
Elec
&
C
om
p
En
g,
V
ol.
8
, N
o.
6
,
Dece
m
ber
2018
:
4735
-
474
4
4742
Figure
4
.
Com
par
at
ive
Analy
sis
4.
CONCL
US
I
O
N
T
he
m
ai
n
i
de
a
of
t
hi
s
pa
pe
r
i
s
t
o
i
nt
r
od
uc
e
a
c
on
c
e
pt
t
ha
t
a
n
a
na
l
y
t
i
c
al
m
od
e
l
i
ng
c
ou
l
d
be
de
s
i
gn
e
d
f
or
ov
e
r
c
om
i
ng
t
he
r
e
s
e
a
r
c
h
pr
o
bl
em
a
s
s
oc
ia
t
e
d
w
i
t
h
r
i
s
k
m
a
ne
gm
e
nt
.
W
e
l
l
,
r
i
s
k
m
a
na
ge
m
e
nt
i
s
a
ve
r
y
va
s
t
c
on
c
e
pt
a
nd
t
h
e
pr
ob
l
e
m
s
a
s
soc
i
a
t
e
d
w
i
t
h
i
ts
a
l
s
o
vo
l
um
i
nous
.
T
hi
s
i
s
a
ls
o
on
e
of
t
he
r
e
a
s
on
t
ha
t
w
hy
t
he
e
xi
s
t
i
ng
r
e
s
e
a
r
c
h
-
ba
s
e
d
a
p
pr
o
a
c
he
s
do
e
s
n’
t
e
nc
a
ps
ul
a
t
e
a
ll
th
e
pr
o
bl
em
s
.
Aft
e
r
r
e
vi
e
w
i
ng
e
xi
s
t
i
ng
a
pp
r
oa
c
he
s
,
i
t
w
a
s
f
ou
nd
t
ha
t
pr
o
bl
em
s
ori
gi
na
t
e
d
f
r
om
t
he
un
c
e
r
t
a
i
nt
y
f
a
c
t
or
of
r
i
s
k
a
r
e
f
ou
nd
no
t
t
o
be
a
dd
r
e
s
s
e
d
a
n
d
he
nc
e
t
hi
s
pr
o
po
s
e
d
P
O
R
M
i
s
de
s
i
gn
e
d
e
xc
l
us
i
ve
l
y
t
o
a
dd
r
e
s
s
t
hi
s
pr
ob
l
em
.
T
he
st
ud
y
ou
t
c
om
e
s
ho
w
s
t
ha
t
pr
o
po
s
e
d P
O
R
M
of
f
e
r
s
g
oo
d
c
on
ve
r
ge
nc
e
b
e
ha
vi
ou
r
p
r
o
vi
ng
t
he
t
e
c
hn
i
c
a
l
c
or
r
e
c
t
ne
s
s
o
f
t
he
pr
op
os
e
d
c
on
c
e
pt
a
nd
i
t
a
l
s
o
pr
o
ve
d
t
o
be
c
om
pu
t
a
t
i
on
a
l
l
y
c
ost
e
f
f
e
c
t
i
ve
a
s
i
t
ha
s
e
xt
r
em
e
ly
ve
r
y
l
e
s
s
it
e
r
a
t
i
on
i
nv
ol
ve
m
e
nt
a
s
w
e
l
l
a
s
it
of
f
e
r
s
f
a
s
t
e
r
pr
oc
e
s
s
i
ng
t
im
e
.
A
pa
r
t
f
r
om
t
hi
s
,
t
he
out
c
om
e
of
c
o
m
pa
r
a
t
i
ve
a
na
l
y
si
s
a
l
s
o
pr
ov
e
d
t
ha
t
i
t
c
ou
l
d
s
uc
c
e
s
s
f
ul
l
y
up
gr
a
de
t
h
e
s
of
t
w
a
r
e
qu
a
l
i
t
y
a
pa
r
t
f
r
om
th
e
r
i
s
k
-
r
e
l
a
t
e
d
pr
o
bl
em
s
.
R
E
F
E
R
E
N
C
E
S
[
1
]
.
C
a
p
e
r
s
J
o
n
e
s
,
S
of
t
w
a
r
e
M
e
t
h
o
d
o
l
o
g
i
e
s
:
A
Q
u
a
n
t
i
t
a
t
i
v
e
G
u
i
d
e
,
C
R
C
P
r
e
s
s
,
2
0
1
7
[
2
]
.
S
a
ï
d
As
s
a
r
,
Im
ed
B
o
u
g
h
z
a
l
a
,
I
sa
b
e
l
l
e
B
o
y
d
e
n
s
,
P
r
a
c
t
i
c
a
l
S
t
u
d
i
e
s
i
n
E
-
G
o
v
e
r
nm
en
t
:
B
e
s
t
Pr
a
c
t
i
c
e
s
f
r
om
A
r
o
un
d
t
h
e
W
o
r
l
d
,
S
p
r
i
ng
e
r
S
c
i
e
n
c
e
&
B
u
s
i
n
e
ss
M
e
d
i
a
,
2
0
1
0
[
3
]
.
A
l
a
i
n
A
p
r
i
l
,
C
l
a
u
d
e
Y
.
L
a
p
o
r
t
e
,
S
o
f
t
w
a
r
e
Q
u
a
l
i
t
y
A
s
s
ur
a
n
c
e
,
J
o
h
n
W
i
l
e
y
&
S
o
n
s
,
2
0
1
8
[
4
]
.
C
a
p
e
r
s
J
o
n
e
s
,
A
G
u
i
d
e
t
o
S
e
l
e
c
t
i
n
g
S
o
f
t
w
a
r
e
M
e
a
s
u
r
e
s
a
n
d
M
e
t
r
i
c
s
,
C
R
C
P
r
e
ss
,
2
0
1
7
[
5
]
.
A
l
e
x
S
i
d
o
r
e
n
k
o
,
E
l
e
n
a
D
e
m
i
d
e
n
k
o
,
G
u
i
d
e
t
o
E
f
f
e
c
t
i
v
e
R
i
s
k
M
a
n
a
ge
m
e
n
t
3
.
0
,
C
r
e
a
t
e
S
p
a
c
e
I
n
d
e
p
e
n
d
e
n
t
P
u
b
l
i
s
h
i
n
g
P
l
a
t
f
o
r
m
,
2
0
1
7
[
6
]
.
A
p
a
r
n
a
G
u
p
t
a
,
R
i
s
k
M
a
n
a
g
e
m
e
n
t
a
n
d
S
i
m
u
l
a
t
i
o
n
,
C
R
C
P
r
e
ss
,
2
0
1
6
[
7
]
.
M
r
K
i
t
S
a
d
g
r
o
v
e
, Th
e
C
o
m
p
l
e
t
e
G
u
i
d
e
t
o
B
u
s
i
n
e
ss
R
i
s
k
M
a
n
a
g
eme
n
t
,
G
o
w
e
r
P
u
b
l
i
s
h
i
n
g
,
2
0
1
5
[
8
]
.
A
.
N
o
r
d
i
n
,
L
.
M.
A
b
d
u
l
l
a
h
,
F
.
D.
M
o
h
a
m
a
d
F
a
d
zil
a
n
d
N
.
A
.
S
.
R
o
s
e
l
a
n
,
"
R
e
q
u
ir
e
m
e
n
t
s
e
l
i
c
i
t
a
t
i
o
n
a
n
d
a
n
a
l
y
s
i
s
:
T
o
w
a
r
d
s
t
h
e
a
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IS
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8708
In
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474
4
4744
BIOGR
AP
HI
ES OF
A
UTH
ORS
Salma
Fi
rdose
did
her
Bac
h
el
o
r
of
Scie
nce
fro
m
Banga
lore
Univer
sit
y
from
2000
to
2003.
In
2003
rec
e
ive
d
the
Ba
che
lor
d
egr
ee.
She
Stu
die
d
Master
s
o
f
Com
pute
r
Applicati
on
from
Banga
lor
e
Univer
sit
y
from
2003
to
2006
and
was
awa
rde
d
m
aste
rs
in
the
sam
e
y
ea
r.
In
2007
t
o
2009
did
Master
of
Philosoph
y
from
Bhara
thi
a
r
Univer
si
y
,
Co
imbatore
.
Now
she
is
a
Ph.D.
student
5th
y
ea
r
of
CS
E
at
Bhara
thi
ar
Univer
sit
y
,
Coim
bat
ore
,
India
.
She
worked
as
le
ct
ure
r
for
8
y
ea
rs
at Ba
ng
a
lore
,
Indi
a and
2
y
e
ars
in
abr
o
ad.
Now
cur
ren
tly
w
orking
a
t
B
anga
l
ore
,
India
Dr.
L.
Manjun
atha
Rao
is
wor
king
as
Profess
or
and
Hea
d
,
Depa
rtment
of
MCA
,
Dr.AIT,
Banga
lor
e.
He
h
as
got
25
y
ears
of
te
a
chi
ng
exp
eri
en
ce.
He
did
his
Bac
he
lor
of
Scie
nc
e
from
Banga
lor
e
Univ
ersity
in
th
e
y
e
ar
1990.
He
St
udie
d
Master
s
of
Com
pute
r
Applicati
on
from
Madhura
i
Kam
ara
j
Univer
sit
y
a
nd
was
awa
rde
d
in
the
y
e
ar
1999.
In
2002
d
id
Master
of
Philosoph
y
from
Mononm
ani
um
Sundara
nar
Univer
sit
y
.
He
has
awa
rde
d
Ph.D
from
Vinay
aka
Miss
ion
Univer
sit
y
,
Ta
m
il
Na
du.
He
has
publi
shed
rese
a
rch
pap
ers
in
bot
h
nat
ional
and
int
ern
at
ion
al
Jou
rna
ls a
nd
h
as
au
thore
d
2
t
ext
boo
ks.
Evaluation Warning : The document was created with Spire.PDF for Python.