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s
a
n
d
to
s
ea
r
ch
a
p
r
o
m
is
i
n
g
s
o
lu
tio
n
b
y
s
i
m
u
la
tin
g
ar
ti
f
ici
al
an
ts
an
d
p
h
er
o
m
o
n
e
lev
el.
T
h
is
is
ac
h
ie
v
ed
b
y
co
n
v
er
ti
n
g
th
e
p
r
o
b
le
m
in
to
g
r
ap
h
ical
f
o
r
m
.
On
th
e
o
th
er
h
a
n
d
,
GA
is
an
ev
o
lu
tio
n
ar
y
ap
p
r
o
ac
h
to
s
ea
r
ch
f
o
r
p
r
o
m
is
in
g
s
et
o
f
s
o
lu
t
i
o
n
s
f
r
o
m
a
p
o
o
l
o
f
p
o
p
u
latio
n
.
I
t
is
in
s
p
ir
ed
f
r
o
m
Dar
w
i
n
’
s
th
eo
r
y
o
f
ev
o
lu
t
io
n
an
d
n
a
tu
r
al
s
e
le
ctio
n
.
T
h
e
p
o
s
s
ib
le
s
o
lu
tio
n
s
o
f
th
e
p
r
o
b
le
m
ar
e
f
ir
s
t
en
co
d
ed
as
ch
r
o
m
o
s
o
m
es
an
d
in
itial
p
o
p
u
latio
n
i
s
cr
ea
ted
.
T
h
e
tw
o
o
p
er
ato
r
s
:
cr
o
s
s
o
v
er
an
d
m
u
t
atio
n
ar
e
ap
p
lied
to
p
r
o
d
u
ce
n
e
w
p
o
p
u
lat
io
n
.
A
f
it
n
es
s
f
u
n
ctio
n
is
c
h
o
s
e
n
to
d
eter
m
in
e
th
e
e
f
f
ec
ti
v
e
n
ess
o
f
n
e
w
g
e
n
er
atio
n
.
Gen
er
ati
o
n
af
ter
g
e
n
er
atio
n
a
n
e
f
f
ec
t
iv
e
s
e
t
o
f
s
o
lu
tio
n
g
r
ad
u
all
y
ev
o
l
v
e
th
r
o
u
g
h
th
is
p
r
o
ce
s
s
s
atis
f
y
in
g
t
h
e
f
it
n
es
s
f
u
n
ct
io
n
an
d
co
n
v
er
g
a
n
ce
is
ac
h
iev
ed
.
W
e
ex
p
lo
ited
th
e
ad
v
a
n
ta
g
es
o
f
ab
o
v
e
m
en
tio
n
ed
tec
h
n
iq
u
es
to
d
ev
elo
p
a
h
y
b
r
id
ap
p
r
o
ac
h
i.e
.
HACG
A
(
H
y
b
r
id
An
t
C
o
lo
n
y
–
Gen
et
ic
A
l
g
o
r
ith
m
)
th
a
t
is
ca
p
ab
le
o
f
s
elec
ti
n
g
p
r
o
m
is
in
g
te
s
t
ca
s
e
s
to
r
ed
u
ce
th
e
s
ize
o
f
te
s
t s
u
ite
w
i
th
o
u
t
co
m
p
r
o
m
is
i
n
g
w
it
h
th
e
e
f
f
icien
c
y
a
n
d
test
co
v
er
ag
e.
2.
R
E
L
AT
E
D
WO
RK
So
f
t
co
m
p
u
tin
g
b
ased
tech
n
i
q
u
es
h
a
v
e
attr
ac
ted
th
e
r
ese
ar
ch
er
s
o
v
er
m
a
n
y
y
ea
r
s
d
u
e
to
th
eir
p
o
ten
tial
to
d
ea
l
w
ith
u
n
ce
r
tai
n
t
y
a
n
d
in
co
m
p
lete
k
n
o
w
led
g
e.
T
h
e
f
ield
o
f
s
o
f
t
w
ar
e
tes
tin
g
o
v
er
c
o
m
p
o
n
en
t
b
ased
s
y
s
te
m
is
also
b
ee
n
in
f
l
u
en
ce
d
w
it
h
th
e
s
e
s
ea
r
ch
b
ase
d
tech
n
iq
u
e
s
an
d
h
as
r
es
u
lted
in
to
a
v
ast
liter
at
u
r
e
an
d
r
esear
ch
w
o
r
k
d
o
n
e
o
v
er
y
ea
r
s
.
A
f
e
w
i
m
p
o
r
tan
t
r
ec
en
t
r
esear
ch
es
o
v
er
last
f
i
v
e
y
ea
r
s
in
th
i
s
f
ield
ar
e
s
u
m
m
ar
ized
h
er
e.
A
b
h
i
s
h
e
k
S
in
g
h
et
a
l
.
in
[4
]
p
r
esen
ted
a
m
o
d
if
ied
g
en
et
ic
alg
o
r
it
h
m
b
ased
tech
n
iq
u
e
f
o
r
test
ca
s
e
g
e
n
er
atio
n
.
T
h
e
y
u
s
e
d
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
(
P
SO)
f
o
r
f
itn
ess
e
n
h
a
n
ce
m
en
t.
Neh
a
et
a
l
.
in
[5
]
ap
p
lied
A
C
O
f
o
r
r
ed
u
cin
g
co
s
t
o
f
r
eg
r
ess
io
n
test
i
n
g
an
d
i
m
p
le
m
en
ted
it
i
n
C
++
.
T
r
ad
itio
n
al
A
C
O
h
a
s
s
ca
r
ce
in
itial
p
h
er
o
m
o
n
e,
k
ee
p
i
n
g
t
h
at
p
o
in
t
in
m
i
n
d
S
h
u
n
k
u
n
Y
an
g
et
a
l
.
in
[6
]
p
r
o
p
o
s
ed
im
p
r
o
v
ed
p
h
er
o
m
o
n
e
d
ep
o
s
i
tio
n
an
d
u
p
d
atio
n
co
ef
f
icie
n
ts
a
n
d
co
m
p
ar
ed
th
e
r
es
u
lts
w
it
h
r
an
d
o
m
te
s
ti
n
g
a
n
d
GA
b
ased
te
s
ti
n
g
.
Var
io
u
s
s
o
f
t
co
m
p
u
ti
n
g
b
ase
d
tech
n
iq
u
es
lik
e
n
e
u
r
al
n
et
w
o
r
k
,
a
n
t
s
y
s
te
m
etc.
ar
e
c
o
m
p
ar
ed
i
n
[7
]
f
o
r
s
o
f
t
w
ar
e
f
au
lt
p
r
ed
ictio
n
.
A
u
t
h
o
r
s
in
[8
]
u
tili
ze
d
p
o
ten
tial
o
f
AC
O
f
o
r
r
ed
u
cin
g
test
ca
s
es
f
o
r
o
b
j
ec
t
o
r
ien
ted
s
y
s
te
m
s
a
n
d
i
m
p
le
m
en
ted
th
eir
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
u
s
i
n
g
MA
T
L
A
B
.
Ma
u
n
ik
a
et
a
l
.
in
[9
]
ex
p
lo
ited
B
ee
co
lo
n
y
o
p
ti
m
izat
io
n
f
o
r
test
ca
s
e
s
elec
tio
n
a
n
d
to
i
m
p
r
o
v
e
p
ath
co
v
er
ag
e.
A
u
th
o
r
s
i
n
[
1
0
]
u
s
ed
g
en
etic
alg
o
r
ith
m
f
o
r
r
eg
r
es
s
io
n
te
s
t
s
u
i
te
p
r
io
r
itizatio
n
a
n
d
p
r
o
d
u
ce
d
m
u
ta
n
ts
f
o
r
o
b
j
ec
t
o
r
ie
n
ted
co
d
es.
W
asiu
r
R
h
m
a
n
n
e
t a
l
.
in
[
1
1
]
p
r
esen
te
d
th
eir
r
esear
ch
i
n
w
h
ich
th
e
y
ap
p
lied
GA
f
o
r
i
m
p
r
o
v
in
g
te
s
t
ef
f
icien
c
y
i
n
ea
r
l
y
s
tag
e
s
o
f
s
o
f
t
w
ar
e
d
ev
elo
p
m
e
n
t.
T
h
e
y
tr
ied
to
im
p
r
o
v
e
test
co
v
er
ag
e
o
f
ac
tiv
it
y
d
ia
g
r
a
m
cr
ea
ted
f
r
o
m
d
esi
g
n
s
p
ec
i
f
icatio
n
.
R
esear
c
h
er
s
ar
e
also
attr
ac
ted
to
w
ar
d
s
t
h
e
ad
ap
tiv
e
b
eh
av
io
r
o
f
A
C
O
i
n
w
h
ic
h
th
e
y
tr
ied
to
m
o
d
i
f
y
t
h
e
al
g
o
r
ith
m
b
ased
o
n
s
o
m
e
p
ar
a
m
eter
s
to
g
et
b
et
ter
r
esu
lt
s
i
n
ca
s
e
o
f
tes
t
ca
s
e
s
elec
tio
n
a
s
d
o
n
e
b
y
[
1
2
-
1
5
]
.
Si
m
ilar
l
y
m
a
n
y
r
esear
ch
er
s
a
n
d
p
r
ac
titi
o
n
er
s
a
r
e
m
o
r
e
attr
ac
ted
to
w
ar
d
s
g
e
n
etic
al
g
o
r
ith
m
f
o
r
s
o
f
t
w
ar
e
test
in
g
an
d
ap
p
lied
th
e
s
a
m
e
at
v
ar
io
u
s
p
h
a
s
es o
f
te
s
tin
g
as i
n
[
1
6
,
17
]
.
A
v
ar
ia
n
t
o
f
G
A
i
s
p
r
ese
n
ted
in
[
1
8
]
as
b
ac
ter
io
lo
g
ic
alg
o
r
ith
m
(
B
A
)
a
n
d
in
tr
o
d
u
ce
d
n
e
w
m
e
m
o
r
izat
io
n
o
p
er
ato
r
.
T
o
co
n
s
id
er
t
h
e
f
ac
t
t
h
at
t
h
er
e
is
a
l
w
a
y
s
a
s
co
p
e
o
f
i
m
p
r
o
v
e
m
en
t,
r
esear
ch
er
s
w
e
n
t
o
n
e
m
o
r
e
s
tep
ah
ea
d
a
n
d
d
ev
elo
p
ed
h
y
b
r
id
tec
h
n
iq
u
es
b
y
co
m
b
i
n
i
n
g
t
w
o
o
r
m
o
r
e
s
o
f
t
co
m
p
u
ti
n
g
b
ased
tech
n
iq
u
es
to
f
u
r
t
h
er
en
h
an
ce
th
e
p
o
ten
t
ial
to
o
p
ti
m
ize
p
r
o
b
lem
s
.
O
n
e
s
u
c
h
r
esear
ch
is
p
r
esen
ted
in
[
1
9
]
w
h
ic
h
ap
p
li
es
cr
o
s
s
o
v
er
b
et
w
ee
n
an
t
s
to
r
ed
u
ce
th
e
r
eg
r
ess
io
n
test
i
n
g
co
s
t.
P
.
Gu
lia
et
a
l
.
in
[
2
0
]
p
r
esen
ted
a
r
ev
ie
w
o
f
all
th
e
s
o
f
t
co
m
p
u
tin
g
b
ased
tech
n
iq
u
e
s
f
o
r
test
i
n
g
r
eu
s
ab
le
co
m
p
o
n
e
n
t
s
an
d
co
n
clu
d
ed
th
at
G
A
an
d
A
C
O
ar
e
t
h
e
p
r
o
m
i
n
en
t
n
atu
r
e
in
s
p
ir
ed
tech
n
iq
u
e
s
t
h
at
attr
ac
ted
r
esear
ch
er
s
in
r
ec
en
t
y
ea
r
s
.
Au
t
h
o
r
s
in
[
2
1
]
p
r
o
p
o
s
ed
a
h
y
b
r
id
ap
p
r
o
ac
h
f
o
r
test
ca
s
e
s
elec
t
io
n
u
s
in
g
f
u
zz
y
i
n
f
er
e
n
ce
s
y
s
te
m
a
n
d
AC
O.
Fu
r
t
h
er
B
ee
co
lo
n
y
o
p
ti
m
izatio
n
(
B
C
O)
h
as
also
attr
ac
ted
r
esear
ch
er
s
as
i
n
[
2
2
]
w
h
er
e
au
t
h
o
r
s
i
m
p
le
m
en
ted
G
A
b
ased
B
C
O
f
o
r
au
to
m
atio
n
o
f
v
ar
io
u
s
te
s
ti
n
g
p
h
a
s
es.
P
alak
et
a
l
.
i
n
[
2
3
]
p
r
o
p
o
s
ed
an
A
C
O
b
ased
m
o
d
el
f
o
r
test
i
n
g
co
m
p
o
n
e
n
t
b
ased
s
o
f
t
w
ar
e
an
d
th
eir
in
ter
ac
tio
n
f
ail
u
r
e.
T
o
s
u
m
m
ar
ize,
a
v
ast
lite
r
atu
r
e
is
av
ailab
le
in
th
is
f
ield
w
h
ich
s
h
o
w
s
its
i
n
d
u
s
tr
ial
i
m
p
o
r
tan
ce
a
n
d
co
v
er
ag
e.
3.
P
RO
P
O
SE
D
M
O
DE
L
I
n
th
i
s
s
ec
tio
n
,
a
h
y
b
r
id
ap
p
r
o
ac
h
is
p
r
o
p
o
s
ed
th
at
co
m
b
in
es
th
e
b
en
e
f
it
s
o
f
AC
O
a
n
d
GA
.
Fir
s
t
o
f
all
th
e
s
y
s
te
m
u
n
d
er
test
(
S
UT
)
is
co
n
v
er
ted
in
to
its
r
es
p
ec
tiv
e
co
m
p
o
n
e
n
t
d
iag
r
a
m
.
T
h
e
m
ai
n
id
ea
is
to
p
o
p
u
late
th
e
s
y
s
te
m
w
ith
s
o
m
e
r
an
d
o
m
a
n
t
s
as
d
o
n
e
i
n
tr
ad
itio
n
a
l
A
C
O.
E
ac
h
a
n
t
w
h
ile
m
o
v
i
n
g
to
th
e
n
ei
g
h
b
o
r
in
g
co
m
p
o
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
6
,
Dec
em
b
er
2
0
1
9
:
4
8
9
8
-
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0
3
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u
ite.
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h
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ed
a
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ig
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r
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m
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ized
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n
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t: Fa
u
lt M
atr
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t D
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n
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te
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t d
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p
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m
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lt o
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f
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r
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ap
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tatio
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s
2
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d
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u
n
til s
to
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p
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iter
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et.
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tp
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t: R
ed
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ce
d
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o
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h
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h
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s
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ased
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w
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p
ar
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eter
s
:
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e
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y
l
i
m
iti
n
g
th
e
to
tal
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ec
u
tio
n
ti
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e
o
f
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h
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tes
t c
ases
; o
t
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er
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y
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ec
k
i
n
g
w
h
et
h
er
all
t
h
e
f
a
u
lt
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h
as b
ee
n
co
v
er
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.
Fig
u
r
e
1
.
P
r
o
p
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ed
m
o
d
el
4.
M
E
T
H
O
DO
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B
ef
o
r
e
ap
p
ly
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th
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p
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p
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ed
tech
n
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d
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ata
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ct
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r
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ata
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ip
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f
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d
n
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m
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atr
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t c
ase
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d
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ed
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h
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e
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v
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h
e
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tr
y
F
i,
j
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lt
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th
e
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ter
s
ec
tio
n
o
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r
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n
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lu
m
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d
eter
m
i
n
e
d
u
s
in
g
t
h
e
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o
llo
w
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n
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n
o
tatio
n
:
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i,
j
=1
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if
T
est ca
s
e
T
i
u
n
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s
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lt
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j
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,
Oth
er
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e
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e
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k
f
i
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teen
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i
f
f
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en
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d
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i
f
teen
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i
n
to
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o
n
s
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er
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n
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i
th
a
s
s
u
m
p
t
io
n
o
f
at
least
o
n
e
f
au
lt
d
etec
ted
p
er
test
ca
s
e.
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h
e
p
r
o
b
lem
o
f
test
ca
s
e
s
elec
tio
n
ca
n
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e
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ie
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ed
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s
el
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b
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s
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e.
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o
f
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tr
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itio
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A
C
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d
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s
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p
lied
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th
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n
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r
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lts
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w
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i
n
F
ig
u
r
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2
.
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h
en
t
h
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p
r
o
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ed
HACG
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(
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r
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n
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p
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n
d
it
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a
s
f
o
u
n
d
th
a
t
th
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s
tech
n
iq
u
e
p
er
f
o
r
m
s
b
etter
th
an
t
h
e
t
r
ad
itio
n
al
tech
n
iq
u
e
s
an
d
r
es
u
lts
ar
e
s
h
o
w
n
i
n
n
ex
t
s
ec
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Hyb
r
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s
w
a
r
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n
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GA
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a
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a
p
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r
s
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ftw
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r
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test
ca
s
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s
elec
tio
n
(
P
a
la
k)
4901
T
ab
le
1
.
Fau
lt
m
atr
i
x
T
e
st
C
a
se
F1
F2
F3
F4
F5
F6
F7
F8
F9
F
1
0
F
1
1
F
1
2
F
1
3
F
1
4
F
1
5
Ex
e
c
u
t
i
o
n
T
i
me
T1
1
0
1
0
0
0
0
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1
0
0
0
0
0
1
6
T2
0
1
0
0
0
0
1
0
1
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0
1
0
0
0
5
T3
1
0
0
0
0
0
0
1
0
0
0
0
1
0
0
4
T4
0
0
0
0
0
1
0
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0
0
1
0
0
0
0
5
T5
1
0
0
0
0
1
0
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0
1
0
0
1
0
5
T6
1
0
1
0
0
0
0
0
0
0
0
0
1
0
0
6
T7
0
1
0
1
0
1
0
0
0
0
0
0
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0
6
T8
0
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1
1
0
1
0
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6
T9
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0
5
T
1
0
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0
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1
1
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T
1
2
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T
1
3
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1
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1
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0
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1
4
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6
T
1
5
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0
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0
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0
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1
0
0
5
5.
RE
SU
L
T
S AN
D
AN
AL
Y
SI
S
T
ab
le
2
s
h
o
w
s
r
ed
u
ce
d
f
a
u
lt
m
atr
i
x
F’i,
j
th
at
co
n
tain
s
s
u
b
s
et
o
f
r
o
w
s
w
it
h
s
elec
ted
tes
t
ca
s
es
af
ter
ap
p
ly
i
n
g
H
AC
G
A
.
T
h
e
g
r
a
p
h
s
h
o
w
n
i
n
F
ig
u
r
e
2
co
m
p
ar
es
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
w
i
th
tr
ad
itio
n
al
tech
n
iq
u
es.
I
n
F
ig
u
r
e
2
,
tr
ad
itio
n
al
A
C
O
a
n
d
tr
ad
itio
n
al
G
A
ar
e
co
m
p
ar
ed
w
i
th
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
HACG
A
in
ter
m
s
o
f
p
er
ce
n
tag
e
o
f
test
ca
s
es
th
at
n
ee
d
to
b
e
ex
ec
u
ted
to
ac
h
iev
e
h
i
g
h
er
p
er
ce
n
tag
e
o
f
f
au
lts
d
etec
tio
n
.
It
w
a
s
r
es
u
lted
th
a
t
H
AC
G
A
b
ased
tech
n
iq
u
e
is
ca
p
ab
le
o
f
ac
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ie
v
in
g
1
0
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f
a
u
lt
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er
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g
e
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n
3
3
%
o
f
te
s
t
ca
s
es.
W
h
ile
tr
ad
itio
n
al
tec
h
n
iq
u
es
u
n
d
er
p
er
f
o
r
m
in
t
h
is
s
c
en
ar
io
.
Fig
u
r
e
3
s
h
o
w
s
co
m
p
ar
is
o
n
o
f
H
A
C
G
A
w
it
h
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e
t
w
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tr
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h
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i
q
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es
o
n
th
e
b
a
s
is
o
n
to
tal
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e
cu
tio
n
ti
m
e
to
d
etec
t
all
th
e
f
a
u
lts
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d
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er
ce
n
ta
g
e
s
av
i
n
g
i
n
e
x
ec
u
t
io
n
ti
m
e.
Gr
ap
h
clea
r
l
y
d
ep
icts
t
h
at
t
h
e
p
r
o
p
o
s
ed
h
y
b
r
id
tech
n
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e
p
er
f
o
r
m
s
b
etter
th
a
n
th
e
tr
ad
itio
n
al
tec
h
n
iq
u
e
an
d
r
esu
lted
i
n
to
b
etter
ti
m
e
s
a
v
i
n
g
.
T
ab
le
2
.
R
ed
u
ce
d
f
au
lt
m
atr
ix
w
it
h
s
e
lecte
d
test
ca
s
e
s
T
e
st
C
a
se
F1
F2
F3
F4
F5
F6
F7
F8
F9
F
1
0
F
1
1
F
1
2
F
1
3
F
1
4
F
1
5
Ex
e
c
u
t
i
o
n
T
i
me
T1
1
0
1
0
0
0
0
0
1
0
0
0
0
0
1
6
T2
0
1
0
0
0
0
1
0
1
0
0
1
0
0
0
5
T5
1
0
0
0
0
1
0
0
0
0
1
0
0
1
0
5
T
1
1
0
0
0
1
1
0
0
0
0
1
0
1
0
0
0
4
T
1
2
0
0
1
0
0
1
0
1
0
0
0
0
1
0
0
5
Fig
u
r
e
2
.
R
esu
lts
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
6
,
Dec
em
b
er
2
0
1
9
:
4
8
9
8
-
4
9
0
3
4902
Fig
u
r
e
3
.
P
er
ce
n
tag
e
s
av
i
n
g
in
ex
ec
u
tio
n
ti
m
e
6.
CO
NCLU
SI
O
N
T
est
ca
s
e
s
elec
tio
n
is
a
n
i
m
p
o
r
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t
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i
t
y
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ed
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ce
t
h
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n
g
e
f
f
o
r
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w
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t
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m
p
r
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m
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in
g
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h
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e
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t
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o
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t
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e.
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n
r
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en
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y
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s
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s
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h
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ased
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r
e
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tech
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ar
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lv
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h
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tech
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tec
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e.
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I
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w
as
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al
y
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d
t
h
at
p
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p
o
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tech
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e
p
er
f
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r
m
s
b
etter
th
an
tr
ad
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al
tech
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e
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s
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ab
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t
w
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[2
]
Do
rig
o
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M
a
rc
o
,
a
n
d
G
ian
n
i
Di
Ca
ro
.
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iza
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1
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9
9
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[3
]
M
it
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h
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ll
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M
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lan
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n
in
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to
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ti
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a
lg
o
rit
h
m
s.
M
IT
p
re
ss
,
1
9
9
8
.
[4
]
A
.
S
in
g
h
,
e
t
a
l
.
,
“
A
h
y
b
rid
A
p
p
ro
a
c
h
o
f
Ge
n
e
ti
c
A
lg
o
rit
h
m
a
n
d
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rti
c
le
S
wa
r
m
T
e
c
h
n
iq
u
e
to
S
o
f
tw
a
r
e
T
e
st
Ca
se
G
e
n
e
r
a
ti
o
n
,
”
I
n
t.
J
.
I
n
n
o
v
.
E
n
g
.
T
e
c
h
n
o
l.
,
v
o
l.
3
,
p
p
.
2
0
8
-
2
1
4
,
2
0
1
4
.
[5
]
N
.
S
e
th
i,
“
A
n
ts
Op
ti
m
iza
ti
o
n
fo
r
M
in
im
a
l
T
e
st
C
a
se
S
e
l
e
c
ti
o
n
a
n
d
P
ri
o
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iza
ti
o
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a
s
to
Re
d
u
c
e
th
e
Co
st
o
f
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g
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ss
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n
T
e
stin
g
,
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o
l.
1
0
0
,
p
p
.
4
8
-
5
4
,
2
0
1
4
.
[6
]
S
.
Ya
n
g
,
e
t
a
l
.
,
“
Im
p
ro
v
e
d
a
n
t
a
lg
o
rit
h
m
s f
o
r
so
f
t
w
a
re
tes
ti
n
g
c
a
se
s
g
e
n
e
ra
ti
o
n
,
”
S
c
i
.
W
o
rld
J
.
,
v
o
l.
2
0
1
4
,
2
0
1
4
.
[7
]
E.
Ert
u
rk
a
n
d
E.
A
k
c
a
p
in
a
r,
“
Ex
p
e
rt
S
y
ste
m
s
w
it
h
A
p
p
li
c
a
ti
o
n
s
A
c
o
m
p
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riso
n
o
f
so
m
e
so
f
t
c
o
m
p
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ti
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g
m
e
th
o
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s
f
o
r
so
f
t
w
a
re
f
a
u
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p
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,
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Exp
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rt
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y
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p
p
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.
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l.
4
2
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p
p
.
1
8
7
2
-
1
8
7
9
,
2
0
1
5
.
[8
]
S
.
K.
M
o
h
a
p
a
tra
a
n
d
S
.
P
ra
sa
d
,
“
T
e
st
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se
Re
d
u
c
ti
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n
Us
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g
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n
t
Co
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y
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ti
m
iz
a
ti
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f
o
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O
b
jec
t
Orie
n
ted
P
r
o
g
ra
m
,
”
In
t.
J
.
El
e
c
tr.
Co
mp
u
t.
En
g
.
,
v
o
l.
5
,
p
p
.
1
4
2
4
-
1
4
3
2
,
2
0
1
5
.
[9
]
M
.
M
o
u
n
ik
a
a
n
d
D.
V
.
Re
d
d
y
,
“
T
e
st
Ca
se
S
e
lec
ti
o
n
F
o
r
P
a
t
h
T
e
stin
g
Us
in
g
Be
e
Co
lo
n
y
Op
ti
m
iz
a
ti
o
n
,
”
p
p
.
1
-
7
,
2
0
1
5
.
[1
0
]
S
.
M
u
sa
,
e
t
a
l
.
,
“
S
o
f
tw
a
r
e
Re
g
re
ss
io
n
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e
st
Ca
se
P
rio
rit
iza
ti
o
n
f
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r
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jec
t
-
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P
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m
s
u
sin
g
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e
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e
ti
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l
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o
rit
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m
w
it
h
Re
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e
d
-
F
it
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e
ss
S
e
v
e
rit
y
,
”
In
d
ia
n
J
.
S
c
i.
T
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c
h
n
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l.
,
v
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l.
8
,
2
0
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5
.
[1
1
]
W
.
Rh
m
a
n
n
,
“
Us
e
o
f
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e
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e
ti
c
A
p
p
ro
a
c
h
f
o
r
T
e
st
Ca
se
P
rio
rit
iza
ti
o
n
f
ro
m
UM
L
Ac
ti
v
it
y
Dia
g
r
a
m
,
”
v
o
l.
1
1
5
,
p
p
.
8
-
1
2
,
2
0
1
5
.
[1
2
]
C.
P
i
n
g
a
n
d
X.
M
in
,
“
S
o
f
twa
re
Tes
ti
n
g
Ca
se
G
e
n
e
ra
ti
o
n
o
f
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n
t
C
o
lo
n
y
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ti
m
iza
ti
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n
Ba
se
d
o
n
Qu
a
n
tu
m
D
y
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a
m
i
c
S
e
lf
-
A
d
a
p
tatio
n
,
”
v
o
l
.
8
,
p
p
.
9
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-
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0
4
,
2
0
1
5
.
[1
3
]
S
.
A
g
a
r
w
a
l,
e
t
a
l
.
,
“
A
u
to
m
a
t
ic
T
e
st Da
ta
Ge
n
e
ra
ti
o
n
-
A
c
h
iev
in
g
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m
a
li
t
y
Us
in
g
A
n
t
-
Be
h
a
v
io
u
r,
”
In
t.
J
.
In
f.
E
d
u
c
.
T
e
c
h
n
o
l
.
,
v
o
l
.
6
,
p
p
.
1
1
7
-
1
2
1
,
2
0
1
6
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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A
.
A
n
sa
ri,
e
t
a
l
.
,
“
Op
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m
ize
d
Re
g
re
ss
io
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T
e
st
Us
in
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T
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s
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iza
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p
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.
[1
5
]
M
.
S
.
Ku
m
a
r
a
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d
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.
S
ri
n
iv
a
s,
“
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n
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Co
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ize
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jec
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ra
m
s,”
In
d
ia
n
J
.
S
c
i
.
T
e
c
h
n
o
l.
,
v
o
l.
9
,
2
0
1
6
.
[1
6
]
E.
Kh
a
n
n
a
,
“
Re
g
re
ss
io
n
T
e
stin
g
b
a
se
d
o
n
G
e
n
e
ti
c
A
lg
o
rit
h
m
s,
”
In
t.
J
.
Co
mp
u
t.
Ap
p
l.
,
v
o
l.
1
5
4
,
p
p
.
4
3
-
4
6
,
2
0
1
6
.
[1
7
]
G
.
Ku
m
a
r
a
n
d
P
.
K.
B
h
a
ti
a
,
“
S
o
f
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w
a
r
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Tes
t
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se
Re
d
u
c
ti
o
n
u
si
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g
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e
n
e
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l
g
o
rit
h
m
:
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M
o
d
if
ied
A
p
p
ro
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h
,
”
In
t.
J
.
In
n
o
v
.
S
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i
.
En
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.
T
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l.
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3
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p
.
3
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4
,
2
0
1
6
.
[1
8
]
P
.
R
.
S
riv
a
sta
v
a
,
“
Tes
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c
a
se
o
p
ti
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is
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ti
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n
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tu
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in
sp
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p
p
ro
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s
in
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b
a
c
terio
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ic
a
lg
o
rit
h
m
,
”
In
t.
J
.
Bi
o
-
In
sp
ire
d
C
o
mp
u
t.
,
v
o
l.
8
,
p
p
.
1
2
2
-
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3
1
,
2
0
1
6
.
[1
9
]
K.
A
ro
ra
,
e
t
a
l
.
,
“
Hy
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ro
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Ba
se
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A
&
A
CO,”
In
t.
J
.
I
n
n
o
v
.
Res
.
T
e
c
h
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o
l
.
,
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l.
3
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p
.
6
5
-
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9
,
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0
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6
.
[2
0
]
P
.
G
u
li
a
a
n
d
P
.
P
a
lak
,
“
Na
tu
re
In
sp
ired
S
o
f
t
Co
m
p
u
ti
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g
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s
e
d
S
o
f
tw
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ti
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h
n
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e
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f
o
r
Re
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sa
b
le
S
o
f
tw
a
re
Co
m
p
o
n
e
n
ts,”
J
.
T
h
e
o
r.
Ap
p
l.
I
n
f.
T
e
c
h
n
o
l.
,
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l.
9
5
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p
.
6
9
9
6
-
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0
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0
1
7
.
[2
1
]
D.
S
il
v
a
,
e
t
a
l
.
,
“
A
H
y
b
rid
A
p
p
ro
a
c
h
f
o
r
T
e
st Cas
e
P
rio
rit
iza
ti
o
n
a
n
d
S
e
lec
ti
o
n
,
”
CEC
,
p
p
.
4
5
0
8
-
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A
CM
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CS
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a
n
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IA
ENG
.
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