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[
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3
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Hig
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[
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8
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.
Sev
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[
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[
3
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5
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C
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d
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An
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it
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ca
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
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C
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I
SS
N:
2088
-
8708
I
d
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Th
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Met
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(
Den
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s
io
n
m
ea
s
u
r
e
m
e
n
t
ar
e
r
ar
el
y
u
s
ed
in
t
h
e
r
ea
l
s
o
f
t
w
ar
e
d
ev
elo
p
m
e
n
t
p
r
o
ce
s
s
.
A
lt
h
o
u
g
h
m
etr
ic
s
ar
e
v
er
y
u
s
e
f
u
l,
th
e
y
h
a
v
e
n
o
t
b
ee
n
h
o
w
ev
er
,
w
id
el
y
e
m
p
lo
y
ed
i
n
i
n
d
u
s
tr
ies
[
9
]
.
B
ec
au
s
e
t
h
er
e
i
s
n
o
t
h
r
es
h
o
ld
o
f
co
h
esio
n
th
at
ca
n
d
i
f
f
er
t
h
e
g
o
o
d
an
d
b
ad
d
esig
n
.
T
h
er
e
is
n
o
i
n
f
o
r
m
atio
n
ab
o
u
t
t
h
e
m
etr
ic
s
t
h
r
es
h
o
ld
th
a
t
ca
n
b
e
u
s
ed
b
y
I
T
p
r
ac
titi
o
n
er
s
[
11
]
.
S
o
f
t
w
ar
e
m
etr
ics
ca
n
b
e
u
s
ed
to
co
n
tr
o
l a
n
d
m
o
n
i
to
r
th
e
p
r
o
j
ec
t e
x
ec
u
tio
n
[
1
2
]
.
T
h
e
p
r
esen
ce
s
tu
d
y
ai
m
s
to
d
eter
m
i
n
e
th
e
th
r
es
h
o
ld
o
f
m
et
r
ic
D3
C
2
in
o
r
d
e
r
to
th
e
I
T
p
r
ac
titi
o
n
er
s
ar
e
ab
le
to
i
m
p
le
m
e
n
t
t
h
e
m
etr
ic
in
th
e
p
r
o
ce
s
s
o
f
d
ev
elo
p
m
e
n
t
s
o
f
t
w
ar
e
s
y
s
te
m
.
T
h
e
s
tu
d
y
p
r
o
d
u
ce
s
t
h
e
f
r
a
m
e
w
o
r
k
to
f
in
d
o
u
t
t
h
e
v
a
lu
e
o
f
co
h
es
io
n
s
th
r
e
s
h
o
ld
.
T
h
e
s
t
u
d
y
is
d
o
n
e
u
s
i
n
g
s
o
m
e
ex
a
m
p
le
o
f
a
c
lass
d
iag
r
a
m
.
T
o
f
in
d
th
e
v
alu
e
o
f
t
h
e
th
r
es
h
o
ld
,
ex
p
er
t o
f
clas
s
d
esig
n
w
ill b
e
i
n
v
o
l
v
ed
.
2.
T
H
E
DI
ST
ANC
E
DE
S
I
G
N
-
B
ASE
D
DI
RE
C
T
CL
ASS C
O
H
E
SI
O
N
(D
3
C
2
)
M
E
T
R
IC
C
o
h
es
io
n
m
etr
ic
i
s
a
m
ea
s
u
r
e
o
f
t
h
e
q
u
ali
t
y
attr
ib
u
te
s
o
f
o
b
j
ec
t
-
o
r
ien
ted
d
esi
g
n
a
n
d
r
e
f
er
s
to
t
h
e
lev
el
w
h
er
e
cla
s
s
m
e
m
b
er
s
ar
e
r
elate
d
.
T
h
e
p
u
r
p
o
s
e
o
f
m
ea
s
u
r
e
m
en
t
co
h
e
s
io
n
c
lass
is
to
g
et
t
h
e
v
al
u
e
o
f
t
h
e
q
u
alit
y
o
f
clas
s
d
esig
n
w
h
er
e
a
h
ig
h
l
y
co
h
es
iv
e
cla
s
s
is
a
g
o
o
d
d
esig
n
[
5
]
.
J
eh
ad
[
2
]
d
ef
in
e
a
cla
s
s
co
h
e
s
io
n
m
etr
ic
ca
l
led
T
h
e
Dis
ta
n
ce
Desi
g
n
-
B
ased
Dir
ec
t
C
la
s
s
C
o
h
e
s
io
n
(
D3
C
2
)
.
T
h
e
D3
C
2
m
etr
ic
u
s
es
th
e
Dir
ec
t
A
ttrib
u
te
T
y
p
e
(
DA
T
)
m
atr
ix
to
m
ea
s
u
r
e
s
th
e
in
ter
ac
tio
n
ca
u
s
ed
b
y
s
h
ar
in
g
attr
ib
u
te
t
y
p
e
b
et
wee
n
m
eth
o
d
,
i
n
ter
ac
tio
n
ca
u
s
e
d
b
y
th
e
e
x
p
ec
ted
u
s
e
o
f
attr
i
b
u
te
w
it
h
i
n
m
et
h
o
d
an
d
i
n
ter
ac
tio
n
b
et
w
ee
n
attr
ib
u
te
a
n
d
m
et
h
o
d
[
2
]
.
T
h
er
e
ar
e
th
r
ee
d
i
f
f
er
en
t
t
y
p
e
o
f
co
h
esi
o
n
ca
u
s
ed
b
y
th
r
e
e
t
y
p
e
o
f
in
ter
ac
tio
n
:
Me
t
h
o
d
-
Me
t
h
o
d
th
r
o
u
g
h
A
ttrib
u
te
C
o
h
esio
n
(
MM
A
C
)
,
Attr
ib
u
te
-
A
ttrib
u
te
C
o
h
e
s
io
n
(
AAC),
an
d
A
ttrib
u
te
-
Me
t
h
o
d
C
o
h
esio
n
(
A
M
C
)
.
D3
C
2
m
et
r
ics
w
ei
g
h
ti
n
g
f
r
o
m
f
in
al
ca
lc
u
latio
n
o
f
MM
A
C
,
AAC,
an
d
A
M
C
.
T
ab
les an
d
Fig
u
r
e
s
ar
e
p
r
esen
ted
ce
n
ter
,
as
s
h
o
w
n
b
elo
w
a
n
d
cited
in
th
e
m
an
u
s
cr
ip
t.
2
.
1
.
M
e
t
ho
d
-
M
et
ho
d t
hro
ug
h At
t
ribute
s
Co
hes
io
n (
M
M
AC)
M
e
t
rics
MM
AC
is
a
p
r
o
ce
s
s
o
f
ca
lc
u
latin
g
th
e
d
ata
w
er
e
tak
e
n
f
r
o
m
th
e
d
ir
ec
t
m
a
tr
ix
attr
ib
u
te
t
y
p
e.
T
h
is
m
et
h
o
d
ca
n
p
r
o
d
u
ce
an
av
er
ag
e
v
al
u
e
o
f
co
h
esio
n
in
t
h
e
p
r
o
g
r
a
m
is
b
ased
o
n
a
co
u
p
le
o
f
m
et
h
o
d
s
.
an
d
it
is
ca
lcu
lated
as
f
o
llo
w
s
(
1
)
W
h
er
e
x
is
a
n
u
m
b
er
o
f
v
alu
e
1
in
th
e
co
lu
m
n
,
j
n
u
m
b
er
o
f
th
e
m
et
h
o
d
in
th
e
m
atr
i
x
,
an
d
l
n
u
m
b
er
o
f
th
e
attr
ib
u
t
e
.
2
.
2
.
An A
t
t
ribute
-
At
t
ribute
Co
h
esio
n (
AAC)
AAC
is
a
p
r
o
ce
s
s
o
f
ca
lc
u
lati
n
g
t
h
e
d
ata
w
er
e
tak
e
n
f
r
o
m
t
h
e
attr
ib
u
te
m
atr
i
x
t
y
p
e.
T
h
is
m
et
h
o
d
ca
n
p
r
o
d
u
ce
an
av
er
a
g
e
v
alu
e
o
f
co
h
esio
n
i
n
t
h
e
p
r
o
g
r
a
m
b
ased
o
n
t
h
e
p
air
attr
ib
u
tes
a
n
d
it
is
ca
lc
u
lated
a
s
fo
llo
w
s
(
2
)
x
is
a
n
u
m
b
er
o
f
v
al
u
e
1
in
r
o
w
s
,
j
n
u
m
b
er
o
f
t
h
e
m
e
th
o
d
in
th
e
m
a
tr
ix
,
a
n
d
l n
u
m
b
er
o
f
t
h
e
class
attr
ib
u
t
e.
2
.
3
.
At
t
ribute
-
M
e
t
ho
d Co
hes
io
n (
AM
C)
A
p
r
o
ce
s
s
o
f
ca
lcu
lati
n
g
th
e
d
ata
w
as
ta
k
en
f
r
o
m
th
e
attr
ib
u
te
m
atr
ix
t
y
p
e.
T
h
is
m
et
h
o
d
ca
n
p
r
o
d
u
ce
an
a
v
er
ag
e
v
al
u
e
o
f
co
h
esio
n
i
n
th
e
p
r
o
g
r
a
m
b
ased
o
n
th
e
i
n
ter
ac
tio
n
o
f
attr
ib
u
tes
an
d
m
eth
o
d
s
.
I
t
i
s
ca
lcu
lated
as
f
o
llo
w
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
SN
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
6
,
Dec
em
b
er
2
0
1
8
:
5
3
1
8
–
5
3
2
5
5320
(
3
)
W
h
er
e
I
n
u
m
b
er
o
f
r
o
w
s
in
t
h
e
m
a
tr
ix
,
j
n
u
m
b
er
o
f
co
lu
m
n
s
in
th
e
m
atr
i
x
,
k
n
u
m
b
er
o
f
t
h
e
m
et
h
o
d
in
th
e
m
atr
i
x
l n
u
m
b
er
A
t
tr
ib
u
te
to
th
e
m
atr
i
x
2
.
4
.
T
he
Di
s
t
a
nce
Desig
n
-
B
a
s
ed
Dire
ct
Cla
s
s
Co
hes
io
n (
D3
C
2
)
M
et
ric
T
h
e
D3
C
2
m
etr
ic
is
d
e
f
i
n
ed
as
th
e
w
eig
h
ted
s
u
m
m
atio
n
o
f
t
h
e
MM
A
C
,
AAC,
an
d
AM
C
m
etr
ics [
5
]
.
T
h
e
D3
C
2
is
d
ef
in
ed
as
f
o
llo
w
s
:
(
4
)
w
h
er
e
MP
is
th
e
n
u
m
b
er
o
f
m
eth
o
d
p
air
s
,
an
d
A
P
is
th
e
a
n
u
m
b
er
o
f
d
i
s
ti
n
ct
attr
ib
u
te
-
t
y
p
e
s
p
air
s
3.
CO
H
E
N’
S K
AP
P
A
CO
E
F
F
I
CIE
N
T
C
o
h
e
n
'
s
k
ap
p
a
co
ef
f
ic
ien
t
p
r
o
p
o
s
ed
b
y
J
ac
o
b
C
o
h
en
in
1
9
6
0
ar
e
c
o
ef
f
icie
n
ts
to
ev
a
lu
ate
th
e
ag
r
ee
m
e
n
t
b
et
w
ee
n
t
h
e
t
w
o
ass
es
s
o
r
s
o
r
ass
e
s
s
m
e
n
t
m
et
h
o
d
s
.
C
o
h
e
n
s
’
s
k
ap
p
as
m
ea
s
u
r
e
th
e
d
eg
r
ee
o
f
ag
r
ee
m
e
n
t
a
n
d
tak
es
i
n
to
ac
co
u
n
t
t
h
e
co
r
r
ec
t
class
i
f
icatio
n
th
at
m
a
y
h
a
v
e
b
ee
n
o
b
tain
ed
b
y
ch
a
n
ce
b
y
w
ei
g
h
ti
n
g
th
e
m
ea
s
u
r
ed
ac
c
u
r
ac
ies
[
1
3
]
.
C
o
h
en
's
Kap
p
a
i
s
a
m
e
th
o
d
o
f
m
ea
s
u
r
i
n
g
th
e
co
r
r
e
ctn
ess
o
f
th
e
d
ata
[
1
4
]
.
C
o
h
en
'
s
k
ap
p
a
co
ef
f
icien
t d
ef
in
ed
f
o
r
m
all
y
a
s
f
o
ll
o
w
s
:
(
5
)
W
h
er
e
P
o
th
e
p
r
o
p
o
r
tio
n
o
f
th
e
s
i
m
ilar
it
y
o
f
o
b
s
er
v
at
io
n
an
d
P
c
is
t
h
e
p
r
o
p
o
r
tio
n
ex
p
ec
ted
b
y
ch
an
ce
.
T
h
en
,
t
h
e
d
ata
o
b
tain
ed
f
r
o
m
o
b
s
er
v
a
tio
n
s
o
f
t
wo
o
b
s
er
v
er
s
d
escr
ib
ed
co
u
n
ted
to
g
et
th
e
Kap
p
a
co
ef
f
icie
n
t
.
T
h
en
,
t
h
e
r
esu
l
t c
an
b
e
in
ter
p
r
eted
as d
escr
ib
e
in
T
ab
le
1.
T
ab
le
1
.
I
n
ter
p
r
etatio
n
T
ab
le
o
f
Kap
p
a
C
o
ef
f
icie
n
t [
1
5
]
Kap
p
a
P
o
r
tio
n
o
f
A
g
r
ee
m
e
n
t
< 0
less
t
h
an
c
h
an
ce
a
g
r
ee
m
en
t
0
.
0
1
–
0
.
2
0
s
lig
h
t a
g
r
ee
m
e
n
t
0
.
2
1
–
0
.
4
0
f
air
ag
r
ee
m
en
t
0
.
4
1
–
0
.
6
0
m
o
d
er
ate
a
g
r
ee
m
e
n
t
0
.
6
1
–
0
.
8
0
s
u
b
s
ta
n
s
ial
ag
r
ee
m
en
t
0
.
8
1
–
1
al
m
o
s
t p
er
f
ec
t a
g
r
ee
m
e
n
t
4.
M
E
T
H
O
DO
L
O
G
Y
T
h
e
d
eter
m
in
a
tio
n
o
f
co
h
e
s
i
o
n
th
r
es
h
o
ld
is
d
o
n
e
in
th
e
i
ter
ativ
e
p
r
o
ce
s
s
.
T
h
e
aim
i
s
to
g
et
th
e
th
r
es
h
o
ld
o
f
th
e
m
etr
ic
v
al
u
e
o
f
D3
C
2
.
T
h
e
v
alu
e
o
f
D3
C
2
m
etr
ic
is
b
et
w
ee
n
0
-
1
.
W
e
h
a
v
e
to
f
i
n
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er
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n
d
ar
y
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et
w
ee
n
g
o
o
d
o
r
b
ad
d
esig
n
.
T
h
e
e
x
p
er
t
is
in
v
o
l
v
ed
in
th
e
p
r
o
ce
s
s
o
f
d
eter
m
in
i
n
g
th
e
t
h
r
es
h
o
ld
.
T
h
e
f
lo
w
o
f
t
h
e
p
r
o
ce
s
s
is
d
escr
i
b
ed
in
f
ig
u
r
e
1
.
T
o
d
o
all
o
f
th
e
p
r
o
ce
s
s
es,
w
e
h
av
e
to
co
llect
s
ev
er
al
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d
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th
at
h
a
v
e
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n
co
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ted
th
e
v
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e
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f
D3
C
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m
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ic.
A
ll
o
f
t
h
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co
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h
as
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lab
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a
g
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d
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ex
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er
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T
h
en
th
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f
lo
w
t
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d
escr
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Fig
u
r
e
1
is
ap
p
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2088
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8708
I
d
en
tifyin
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Th
r
esh
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fo
r
Dis
ta
n
ce
Desig
n
-
b
a
s
ed
Dir
ec
t Cl
a
s
s
C
o
h
esio
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(
D3
C
2
)
Met
r
ics
(
Den
n
y
S
a
g
ita
)
5321
Fig
u
r
e
1
.
T
h
e
Flo
w
o
f
Dete
r
m
in
i
n
g
T
h
r
es
h
o
ld
T
h
e
p
r
o
ce
s
s
is
d
o
n
e
iter
ativ
el
y
u
n
til
t
h
e
b
est
s
co
r
e
o
f
Kap
p
a
co
ef
f
icie
n
t
is
o
b
tain
ed
.
Af
te
r
th
e
b
est
Kap
p
a
is
f
o
u
n
d
,
th
e
f
i
n
al
p
r
o
ce
s
s
is
d
eter
m
i
n
in
g
t
h
e
t
h
r
es
h
o
ld
.
T
h
e
b
est
Kap
p
a
m
ea
n
s
t
h
a
t
in
t
h
at
’
s
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o
in
t
o
f
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ld
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th
e
d
e
g
r
ee
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ag
r
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e
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t
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ee
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s
y
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m
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x
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h
i
g
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est.
A
lo
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o
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at
a
h
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n
f
o
r
m
a
n
ce
r
esu
lt
w
it
h
t
h
e
ex
p
er
t
5.
DATAS
E
T
AND
T
E
S
T
I
NG
SCE
NA
RIO
T
h
e
d
ata
u
s
ed
in
t
h
is
s
t
u
d
y
ar
e
5
0
class
es
d
o
w
n
lo
ad
ed
f
r
o
m
v
ar
y
i
n
g
s
o
u
r
ce
f
r
o
m
t
h
e
i
n
ter
n
et.
T
h
e
f
o
llo
w
in
g
is
a
lis
t
o
f
w
e
b
s
ites
t
h
at
b
ec
o
m
e
a
s
o
u
r
ce
:
cr
ea
tel
y
.
co
m
,
ib
m
.
co
m
,
co
d
e
-
p
r
o
j
ec
t.c
o
m
,
k
u
w
atala
b
.
co
m
,
j
av
a
w
o
r
ld
.
co
m
,
an
d
j
av
ac
o
d
eg
ee
k
s
.
co
m
.
E
v
er
y
clas
s
h
a
s
a
v
ar
iet
y
o
f
m
eth
o
d
an
d
attr
ib
u
te.
T
h
is
s
a
m
p
le
clas
s
is
g
e
n
er
ated
to
th
e
XM
L
f
o
r
m
a
t
w
it
h
Vi
s
u
al
P
ar
ad
ig
m
So
f
t
w
ar
e.
T
h
er
e
ar
e
t
w
o
s
ce
n
ar
io
s
t
o
id
en
ti
f
y
t
h
e
th
r
es
h
o
ld
,
f
ir
s
t
s
ce
n
ar
io
,
w
e
w
il
l
test
5
0
class
u
s
in
g
a
s
o
f
t
w
ar
e
ap
p
lica
tio
n
ca
ll
C
o
h
e
s
io
n
A
p
p
licatio
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Me
ter
s
h
o
w
n
a
s
F
ig
u
r
e
2
to
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lc
u
late
t
h
e
v
a
lu
e
o
f
co
h
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n
.
T
h
is
s
o
f
t
w
ar
e
is
i
m
p
le
m
e
n
ted
D3
C
2
m
etr
ic
to
ev
al
u
ate
d
ata
class
s
a
m
p
le
f
r
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m
X
ML
f
o
r
m
at
b
ase
d
o
n
j
av
a
p
latf
o
r
m
.
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h
e
s
ec
o
n
d
s
ce
n
ar
io
i
s
w
e
a
s
k
f
o
r
an
e
x
p
er
t
s
o
f
t
w
ar
e
d
esig
n
er
to
tes
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th
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s
a
m
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d
ata
class
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n
d
d
eter
m
in
e
w
h
e
th
er
ea
c
h
cla
s
s
test
ed
h
ad
g
o
o
d
o
r
b
ad
co
h
esi
o
n
.
T
h
e
m
ai
n
p
u
r
p
o
s
e
o
f
th
i
s
t
est
i
s
to
d
eter
m
in
e
th
e
s
i
m
ilar
it
y
b
et
w
ee
n
co
h
esio
n
m
ea
s
u
r
e
m
e
n
ts
ca
r
r
ied
o
u
t b
y
ex
p
er
ts
an
d
test
ed
b
y
u
s
i
n
g
t
h
e
s
y
s
te
m
.
Fig
u
r
e
2
.
C
o
h
esio
n
Me
ter
A
p
p
licatio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
IS
SN
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
6
,
Dec
em
b
er
2
0
1
8
:
5
3
1
8
–
5
3
2
5
5322
6.
RE
SU
L
T
AND
ANA
L
YS
I
S
6
.
1
.
F
ir
s
t
Scena
rio
Resul
t
I
n
th
e
f
ir
s
t
s
ce
n
ar
io
,
w
e
p
er
f
o
r
m
b
y
ca
lcu
lati
n
g
5
0
d
ata
s
et
class
d
ia
g
r
a
m
s
to
th
e
C
o
h
e
s
io
n
Me
ter
A
p
p
licatio
n
.
W
e
co
llect
d
ata
s
et
f
r
o
m
v
ar
y
i
n
g
s
o
u
r
ce
f
r
o
m
in
ter
n
et.
A
ll
c
lass
d
iag
r
a
m
i
s
r
ed
r
a
w
b
y
u
s
i
n
g
C
o
m
p
u
ter
A
id
ed
So
f
t
w
ar
e
E
n
g
in
ee
r
i
n
g
(
C
A
SE)
ca
lled
Vis
u
al
P
ar
ad
ig
m
f
o
r
g
ett
in
g
clas
s
d
iag
r
a
m
i
n
XM
L
Fo
r
m
at.
C
o
h
e
s
io
n
Me
ter
A
p
p
l
icatio
n
is
j
av
a
b
ased
s
o
f
t
w
ar
e
f
o
r
ca
lcu
late
co
h
e
s
io
n
v
alu
e
f
r
o
m
cla
s
s
d
i
g
r
a
m
i
n
XM
L
Fo
r
m
at.
W
e
i
m
p
le
m
e
n
t
th
e
D3
C
2
m
e
tr
ics
f
o
r
ca
lcu
lat
e
th
e
co
h
esio
n
v
al
u
e.
I
n
id
en
t
i
f
y
in
g
t
h
e
attr
ib
u
tes
an
d
o
p
er
atio
n
s
,
w
e
u
s
ed
x
p
at
h
f
u
n
ctio
n
ta
k
en
f
r
o
m
j
av
ax
.
x
m
l.x
p
at
h
lib
r
ar
y
.
XP
at
h
,
w
h
er
e
th
is
f
u
n
ctio
n
i
s
u
s
ed
to
p
ar
s
e
th
e
co
n
ten
ts
o
f
f
ile
s
o
f
t
y
p
e
x
m
l
to
co
n
f
i
g
u
r
e
th
e
ta
g
y
o
u
w
a
n
t
to
r
e
ad
,
b
o
th
attr
ib
u
tes,
o
p
er
atio
n
s
an
d
r
elatio
n
s
h
ip
s
b
et
w
ee
n
t
h
e
t
w
o
.
So
t
h
at
t
h
e
p
r
o
ce
s
s
o
f
id
en
ti
f
y
i
n
g
th
e
attr
ib
u
tes
an
d
o
p
er
atio
n
s
ca
n
b
e
ea
s
il
y
r
ea
d
b
y
th
e
ap
p
li
ca
tio
n
.
Fig
u
r
e
3
s
h
o
w
s
t
h
e
r
esu
lt
s
o
f
ca
lcu
latio
n
o
f
th
e
v
alu
e
o
f
t
h
e
co
h
esio
n
g
e
n
er
ate
f
r
o
m
co
h
e
s
io
n
m
e
ter
ap
p
licatio
n
.
T
h
e
co
h
esio
n
v
al
u
e
p
r
o
d
u
ce
d
h
as
a
m
i
n
i
m
u
m
s
ca
le
o
f
0
to
v
alu
e
th
e
m
a
x
i
m
u
m
is
1
.
I
n
th
i
s
tes
t
th
er
e
ar
e
1
7
d
ata
test
th
at
h
as
v
alu
e
co
h
esio
n
0
,
w
h
ic
h
m
ea
n
s
th
e
m
et
h
o
d
o
n
1
7
d
ata
test
h
as
n
o
p
ar
am
eter
s
an
d
r
etu
r
n
t
y
p
e
at
al
l.
Fig
u
r
e
3
.
R
esu
lts
o
f
ca
lc
u
lat
io
n
f
r
o
m
co
h
esi
o
n
m
eter
6
.
2
.
F
irst
Scena
rio
Resul
t
I
n
th
e
s
ec
o
n
d
s
ce
n
ar
io
w
e
i
n
v
o
lv
ed
ex
p
er
t
to
en
s
u
r
e
a
co
h
e
s
io
n
v
al
u
e
o
f
t
h
e
cla
s
s
t
h
at
is
u
s
ed
as
a
d
ata
s
a
m
p
le
in
t
h
e
tes
t
ap
p
lica
tio
n
h
as
a
h
ig
h
d
e
g
r
ee
o
f
co
h
e
s
iv
e
n
es
s
o
r
n
o
t.
E
x
p
er
ts
w
ill
e
x
a
m
in
e
o
n
e
b
y
o
n
e
s
a
m
p
le
clas
s
w
it
h
o
u
t
n
o
tice
o
r
s
ee
th
e
test
r
es
u
lt
s
f
r
o
m
th
e
a
p
p
licatio
n
o
f
co
h
esio
n
m
eter
s
.
T
ab
le
2
.
Ko
h
en
s
Kap
p
a
K
O
H
EN
S
K
A
P
P
A
Ex
p
e
r
t
S
y
st
e
m
G
o
o
d
B
a
d
T
o
t
a
l
G
o
o
d
12
5
17
B
a
d
15
18
33
T
o
t
a
l
27
23
50
B
ased
o
n
m
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s
u
r
e
m
e
n
t
s
tak
e
n
b
y
t
h
e
e
x
p
er
ts
s
h
o
w
n
i
n
T
ab
le
2
,
th
er
e
ar
e
2
7
class
h
as a
g
o
o
d
lev
el
o
f
co
h
esi
v
en
e
s
s
a
n
d
t
h
e
2
3
clas
s
h
a
s
a
p
o
o
r
lev
el
o
f
co
h
esi
v
en
es
s
.
Fro
m
th
e
r
es
u
lts
o
f
t
h
e
f
ir
s
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n
d
s
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n
d
s
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n
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test
,
ca
n
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e
ta
k
e
n
a
s
ce
n
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io
a
n
al
y
s
is
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h
at
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h
e
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s
s
o
f
5
0
s
a
m
p
les
test
ed
b
y
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an
d
th
er
e
ar
e
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2
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o
f
ap
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licatio
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th
at
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to
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ig
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o
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e
an
d
1
8
class
ag
r
ee
d
w
it
h
a
lo
w
co
h
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v
al
u
e.
W
h
ile
th
er
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3
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Det
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Va
lues
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etter
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an
d
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a.
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al
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e
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g
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is
u
s
ed
as
a
t
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m
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
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DIS
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s
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Evaluation Warning : The document was created with Spire.PDF for Python.
IS
SN
:
2
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8
8
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I
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f
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7
8
to
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ak
e
s
o
a
p
er
f
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t
s
co
r
e.
So
m
e
th
i
n
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s
ca
n
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ca
p
tu
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ed
as a
ca
u
s
e
o
f
d
is
a
g
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m
en
t b
et
w
ee
n
t
h
e
s
y
s
te
m
an
d
t
h
e
ex
p
er
t.
E
x
p
er
t a
s
s
e
s
s
t
h
e
le
v
el
o
f
co
h
e
s
io
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o
f
a
clas
s
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ased
o
n
e
x
p
er
i
en
ce
.
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h
e
lev
el
o
f
co
h
e
s
io
n
o
f
a
class
is
th
e
d
eg
r
ee
o
f
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s
e
n
es
s
b
et
w
e
en
th
e
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m
e
n
ts
i
n
t
h
e
class
.
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h
ese
ele
m
e
n
ts
ar
e
th
e
attr
ib
u
te
s
an
d
m
et
h
o
d
s
o
f
a
class
.
I
f
th
e
c
lo
s
en
e
s
s
b
et
w
ee
n
th
e
attr
ib
u
tes a
n
d
m
e
th
o
d
s
o
f
a
class
h
i
g
h
er
t
h
e
n
it c
a
n
b
e
s
a
id
th
at
a
h
i
g
h
le
v
el
o
f
class
co
h
esio
n
.
I
f
all
t
h
e
at
tr
ib
u
tes
ar
e
m
an
a
g
ed
b
y
t
h
e
w
h
o
le
m
et
h
o
d
w
h
ic
h
is
o
w
n
e
d
b
y
t
h
e
cla
s
s
,
it
ca
n
b
e
co
n
clu
d
ed
th
at
th
e
clo
s
en
e
s
s
b
et
w
ee
n
th
e
m
et
h
o
d
an
d
attr
ib
u
tes
is
h
ig
h
.
D3
C
2
Me
tr
ics
o
n
l
y
lo
o
k
at
th
e
d
ata
ty
p
e
o
f
th
e
p
ar
a
m
eter
f
r
o
m
a
m
e
th
o
d
.
I
f
th
e
d
ata
ty
p
e
o
f
a
m
et
h
o
d
is
th
e
s
a
m
e
as
th
e
d
ata
ty
p
e
o
f
th
e
attr
ib
u
tes o
f
t
h
e
clas
s
,
th
e
n
it i
s
ass
u
m
ed
th
at
t
h
e
m
e
th
o
d
to
m
an
a
g
e
t
h
ese
attr
ib
u
tes.
Ho
w
e
v
er
,
ex
p
er
ts
ar
e
n
o
t
as
s
i
m
p
le
a
s
t
h
at
i
n
a
s
s
e
s
s
i
n
g
t
h
e
p
r
o
x
i
m
it
y
b
et
w
ee
n
th
e
m
eth
o
d
s
an
d
attr
ib
u
tes.
C
lear
er
in
f
o
r
m
atio
n
n
ee
d
ed
,
w
h
eth
er
it
is
tr
u
e
t
h
a
t
an
attr
ib
u
te
is
m
a
n
ag
ed
b
y
a
m
et
h
o
d
.
No
t
o
n
l
y
o
n
th
e
b
asis
o
f
s
i
m
ilar
it
y
t
y
p
e
it.
B
ec
au
s
e
t
h
e
t
y
p
e
p
ar
a
m
eter
o
f
a
m
et
h
o
d
ca
n
b
e
a
s
o
u
r
ce
o
f
o
th
er
d
ata
t
h
at
is
n
o
t
an
attr
ib
u
te
o
f
a
cla
s
s
.
T
h
e
ce
r
tain
t
y
w
h
et
h
er
t
h
e
m
e
th
o
d
r
ea
lly
m
a
n
ag
e
a
ttrib
u
te
s
ca
n
b
e
s
ee
n
f
r
o
m
t
h
e
s
o
u
r
ce
co
d
e
o
f
t
h
e
m
eth
o
d
.
H
o
w
e
v
er
,
a
li
m
itatio
n
o
f
t
h
i
s
s
t
u
d
y
i
s
t
h
e
le
v
el
d
esi
g
n
i
n
w
h
i
ch
t
h
e
d
eter
m
i
n
atio
n
is
b
ased
o
n
t
h
e
co
h
e
s
io
n
o
f
t
h
e
class
d
ia
g
r
a
m
o
n
l
y
.
I
n
t
h
i
s
c
ase,
th
er
e
s
h
o
u
ld
b
e
a
m
o
r
e
i
n
-
d
ep
th
i
n
f
o
r
m
atio
n
th
at
ca
n
b
e
ex
tr
ac
ted
f
r
o
m
t
h
e
class
d
iag
r
a
m
,
w
h
ich
s
h
o
w
s
t
h
at
a
m
eth
o
d
is
d
ef
in
i
tel
y
m
a
n
a
g
e
an
attr
ib
u
te.
I
n
th
e
p
r
o
ce
s
s
o
f
a
n
al
y
zi
n
g
a
c
lass
,
an
e
x
p
er
t
v
ie
w
o
f
s
o
m
e
t
h
in
g
s
.
I
n
ad
d
itio
n
to
th
e
s
a
m
e
p
ar
am
eter
t
y
p
es
w
it
h
attr
ib
u
te
t
y
p
es,
ex
p
er
ts
also
s
ee
f
r
o
m
t
h
e
n
a
m
i
n
g
attr
ib
u
tes
a
n
d
m
et
h
o
d
s
.
Na
m
i
n
g
s
i
m
ilar
it
y
o
r
s
i
m
ilar
it
y
o
f
m
ea
n
in
g
b
et
w
ee
n
t
h
e
s
a
m
e
n
a
m
in
g
attr
ib
u
tes
an
d
m
et
h
o
d
s
ca
n
b
e
ass
u
m
ed
th
at
t
h
e
m
et
h
o
d
s
to
m
an
a
g
e
t
h
ese
attr
ib
u
te
s
.
A
s
well
as
s
o
m
e
o
f
th
e
f
ea
t
u
r
es
p
r
o
v
id
ed
b
y
t
h
e
J
av
a
lan
g
u
ag
e
p
r
o
g
r
am
m
i
n
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to
o
ls
,
w
h
ic
h
u
s
er
s
ca
n
p
er
f
o
r
m
au
to
m
atic
co
d
e
g
e
n
er
atio
n
b
ased
o
n
attr
ib
u
tes
t
h
at
h
a
v
e
b
ee
n
d
e
f
in
ed
.
Gen
er
atio
n
o
f
g
etter
s
a
n
d
s
etter
s
ar
e
o
f
ten
u
s
ed
b
y
d
ev
e
lo
p
er
s
to
m
ak
e
it
ea
s
ier
to
d
ef
i
n
e
m
e
th
o
d
s
.
Na
m
i
n
g
m
eth
o
d
cu
s
to
m
ized
w
it
h
th
e
n
a
m
e
o
f
th
e
g
e
n
er
atio
n
o
f
t
h
e
attr
ib
u
t
es
th
at
h
a
v
e
b
ee
n
d
ef
i
n
ed
.
T
h
er
e
is
a
m
is
m
atc
h
b
et
w
ee
n
t
h
e
m
atr
ix
co
h
esio
n
p
er
s
p
ec
tiv
e
u
s
ed
b
y
t
h
e
e
x
p
er
t
p
er
s
p
ec
tiv
e
i
n
a
n
al
y
z
in
g
t
h
e
class
i
n
t
h
e
le
v
el
d
esig
n
.
I
n
f
u
t
u
r
e
w
o
r
k
,
n
ee
d
s
t
o
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e
ad
d
s
o
m
e
a
s
p
ec
t li
k
e
t
h
e
s
i
m
ilar
it
y
m
ea
n
i
n
g
f
r
o
m
attr
ib
u
te
a
n
d
m
et
h
o
d
f
o
r
ca
lcu
lati
n
g
co
h
esio
n
.
8.
CO
NCLU
SI
O
N
B
ased
o
n
r
esear
ch
th
at
h
as b
ee
n
d
o
n
e
it c
an
b
e
co
n
cl
u
d
ed
as f
o
llo
w
s
:
1.
I
n
id
en
tify
i
n
g
th
e
attr
ib
u
te
s
an
d
o
p
er
atio
n
s
,
w
e
u
s
ed
x
p
ath
f
u
n
c
tio
n
tak
e
n
f
r
o
m
j
av
ax
.
x
m
l.
x
p
ath
lib
r
ar
y
.
XP
ath
,
w
h
er
e
th
is
f
u
n
ctio
n
is
u
s
ed
to
p
ar
s
e
th
e
co
n
t
en
ts
o
f
f
i
le
s
o
f
t
y
p
e
x
m
l
to
co
n
f
ig
u
r
e
th
e
tag
y
o
u
w
a
n
t
to
r
ea
d
,
b
o
th
attr
ib
u
tes,
o
p
er
atio
n
s
an
d
r
elatio
n
s
h
ip
s
b
et
w
ee
n
th
e
t
w
o
.
So
th
at
th
e
p
r
o
ce
s
s
o
f
id
en
ti
f
y
in
g
t
h
e
attr
ib
u
te
s
an
d
o
p
er
atio
n
s
ca
n
b
e
ea
s
il
y
r
ea
d
b
y
t
h
e
ap
p
licatio
n
2.
Dete
r
m
i
n
i
n
g
s
u
cc
e
s
s
f
u
l
o
r
u
n
s
u
cc
es
s
f
u
l
o
n
th
e
te
s
ti
n
g
o
f
test
d
ata
d
eter
m
i
n
ed
o
n
co
h
esio
n
v
alu
e
s
o
b
tain
ed
f
r
o
m
t
h
e
ca
lc
u
latio
n
C
o
h
es
io
n
A
p
p
licatio
n
Me
ter
is
>
0
.
0
0
.
A
n
al
y
s
is
o
f
6
6
%
o
f
th
e
5
0
tes
t
d
ata
in
d
icate
th
e
s
u
cc
es
s
ca
lc
u
latio
n
t
h
at
g
e
n
er
ates
a
v
al
u
e
o
f
co
h
esio
n
.
Me
a
n
w
h
ile,
3
4
% o
f
t
h
e
5
0
te
s
t
d
ata
s
h
o
w
s
t
h
er
e
is
n
o
r
elatio
n
to
th
e
co
h
esio
n
o
f
th
e
cla
s
s
d
i
ag
r
a
m
.
3.
I
n
o
r
d
er
to
d
eter
m
i
n
e
a
m
ea
s
u
r
ab
le
cr
iter
io
n
in
en
s
u
r
in
g
t
h
e
co
h
esio
n
v
alu
e
s
in
a
cla
s
s
,
w
e
d
eter
m
i
n
ed
th
e
th
r
es
h
o
ld
u
s
i
n
g
th
e
ap
p
r
o
ac
h
C
o
h
e
n
s
's
Kap
p
a
an
d
ca
n
b
e
d
r
aw
n
a
co
n
clu
s
io
n
t
h
at
th
e
v
alu
e
o
f
0
.
4
1
is
th
e
b
est t
h
r
es
h
o
ld
v
al
u
e
f
o
r
p
r
ed
ictin
g
a
v
al
u
e
o
f
co
h
e
s
io
n
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
au
th
o
r
s
g
r
atef
u
ll
y
ac
k
n
o
w
led
g
e
th
e
L
ab
o
r
ato
r
y
tea
m
o
f
So
f
t
w
ar
e
E
n
g
i
n
ee
r
in
g
in
Facu
lt
y
o
f
C
o
m
p
u
ter
Scie
n
ce
.
T
h
is
w
o
r
k
w
as
s
u
p
p
o
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ted
in
p
ar
t
b
y
t
he
State
B
u
d
g
et
-
Op
er
atio
n
al
Ass
i
s
ta
n
ce
Un
i
v
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s
itie
s
Sc
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e
m
e
(
Gr
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n
t n
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.
:
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.
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5
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RE
F
E
R
E
NC
E
S
[1
]
R.
S
.
P
re
ss
m
a
n
,
S
o
f
tw
a
r
e
En
g
in
e
e
rin
g
:
A
P
ra
c
ti
ti
o
n
e
r’s A
p
p
ro
a
c
h
,
S
e
v
e
n
th
E
d
.
M
c
G
ra
w
-
Hill
,
2
0
1
5
.
[2
]
J.
A
l
Da
ll
a
l,
“
A
d
e
sig
n
-
b
a
se
d
c
o
h
e
si
o
n
m
e
tri
c
f
o
r
o
b
jec
t
-
o
rien
ted
c
las
se
s,”
In
t.
J
.
Co
mp
u
t
.
S
c
i
.
,
v
o
l.
1
,
n
o
.
3
,
p
p
.
195
–
2
0
0
,
2
0
0
7
.
[3
]
J.
A
l
Da
ll
a
l
a
n
d
L
.
C.
Brian
d
,
“
A
n
o
b
jec
t
-
o
rien
ted
h
ig
h
-
lev
e
l
d
e
sig
n
-
b
a
se
d
c
las
s
c
o
h
e
sio
n
m
e
tr
ic,”
In
f.
S
o
ft
w
.
T
e
c
h
n
o
l
.
,
v
o
l
.
5
2
,
n
o
.
1
2
,
p
p
.
1
3
4
6
–
1
3
6
1
,
2
0
1
0.
[4
]
I.
Ch
o
w
d
h
u
ry
a
n
d
M
.
Z
u
lk
e
rn
i
n
e
,
“
Us
in
g
c
o
m
p
lex
it
y
,
c
o
u
p
li
n
g
,
a
n
d
c
o
h
e
sio
n
m
e
tri
c
s
a
s
e
a
rl
y
in
d
ica
to
rs
o
f
v
u
ln
e
ra
b
il
it
ies
,
”
J
.
S
y
st.
Arc
h
i
t
.
,
v
o
l.
5
7
,
n
o
.
3
,
p
p
.
2
9
4
–
3
1
3
,
2
0
1
1
.
[5
]
J.
A
l
Da
ll
a
l,
“
M
e
a
su
rin
g
th
e
d
isc
rim
in
a
ti
v
e
p
o
w
e
r
o
f
o
b
jec
t
-
o
rien
t
e
d
c
las
s
c
o
h
e
sio
n
m
e
tri
c
s,”
IEE
E
T
ra
n
s.
S
o
ft
w
.
En
g
.
,
v
o
l.
3
7
,
n
o
.
6
,
p
p
.
7
8
8
–
8
0
4
,
2
0
1
1
.
[6
]
Z.
Ch
e
n
,
Y.
Zh
o
u
,
B.
X
u
,
J.
Zh
a
o
,
a
n
d
H.
Ya
n
g
,
“
A
No
v
e
l
A
p
p
ro
a
c
h
to
M
e
a
su
rin
g
Clas
s
C
o
h
e
sio
n
Ba
se
d
o
n
De
p
e
n
d
e
n
c
e
A
n
a
l
y
sis,”
In
t.
Co
n
f.
S
o
f
tw.
M
a
i
n
t
,
p
p
.
3
7
7
–
3
8
4
,
2
0
0
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
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C
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I
SS
N:
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8708
I
d
en
tifyin
g
Th
r
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ld
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Dis
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Desig
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Dir
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C
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D3
C
2
)
Met
r
ics
(
Den
n
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a
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5325
[7
]
B.
S
il
v
a
,
C.
S
a
n
t’an
n
a
,
N.
Ro
c
h
a
,
a
n
d
C.
Ch
a
v
e
z
,
“
T
h
e
e
ffe
c
t
o
f
a
u
to
m
a
ti
c
c
o
n
c
e
rn
m
a
p
p
in
g
stra
teg
ie
s
o
n
c
o
n
c
e
p
tu
a
l
c
o
h
e
sio
n
m
e
a
su
re
m
e
n
t,
”
In
f.
S
o
ft
w.
T
e
c
h
n
o
l
.
,
v
o
l.
7
5
,
p
p
.
5
6
–
7
0
,
2
0
1
6
.
[8
]
Y.
Ya
n
g
,
Y.
Zh
a
o
,
C.
L
iu
,
H.
L
u
,
Y.
Zh
o
u
,
a
n
d
B.
Xu
,
“
A
n
e
m
p
i
ri
c
a
l
in
v
e
stig
a
ti
o
n
in
to
t
h
e
e
ffe
c
t
o
f
slice
t
y
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e
s
o
n
slice
-
b
a
se
d
c
o
h
e
sio
n
m
e
tri
c
s,”
In
f.
S
o
ft
w.
T
e
c
h
n
o
l.
,
v
o
l.
7
5
,
p
p
.
9
0
–
1
0
4
,
2
0
1
6
.
[9
]
M
.
A
.
T
o
rk
a
m
a
n
i,
“
M
e
tri
c
S
u
it
e
to
Ev
a
lu
a
te
Re
u
sa
b
i
li
ty
o
f
S
o
f
t
w
a
r
e
P
r
o
d
u
c
t
L
in
e
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
),
V
o
l
4
,
No
.
2
,
p
p
2
8
5
-
2
9
4
.
2
0
1
4
[1
0
]
K.
A
.
M
.
F
e
rre
ira,
M
.
A
.
S
.
Big
o
n
h
a
,
R.
S
.
Big
o
n
h
a
,
L
.
F
.
O.
M
e
n
d
e
s,
a
n
d
H.
C.
A
lm
e
id
a
,
“
Id
e
n
ti
f
y
in
g
th
re
sh
o
ld
s
f
o
r
o
b
jec
t
-
o
rien
ted
so
f
tw
a
re
m
e
tri
c
s,”
J
.
S
y
st.
S
o
ft
w
.
,
v
o
l.
8
5
,
n
o
.
2
,
p
p
.
2
4
4
–
2
5
7
,
2
0
1
2
.
[1
2
]
M
.
Bh
a
rd
w
a
j
a
n
d
A
.
Ra
n
a
,
“
K
e
y
S
o
f
t
w
a
r
e
M
e
tri
c
s
a
n
d
it
s
Im
p
a
c
t
o
n
e
a
c
h
o
th
e
r
f
o
r
S
o
f
tw
a
r
e
De
v
e
lo
p
m
e
n
t
P
r
o
jec
ts
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
mp
u
ter
E
n
g
i
n
e
e
rin
g
(
IJ
ECE
),
Vo
l
6
,
N
o
.
1
,
p
p
2
42
-
2
4
8
.
2
0
1
6
[1
3
]
P
ra
sa
d
,
S
,
S
a
v
it
h
ri
S
,
Krish
n
a
“
Co
m
p
a
riso
n
o
f
Ac
c
u
ra
c
y
M
e
a
su
re
s
f
o
r
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m
a
g
e
Clas
sif
i
c
a
ti
o
n
u
sin
g
S
V
M
a
n
d
A
N
N
Clas
si
f
iers
”
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
),
V
o
l
7
,
No
.
3
,
p
p
1
1
8
0
-
1
1
8
7
.
2
0
1
7.
[1
4
]
J.
S
im
a
n
d
C.
C.
W
rig
h
t,
“
T
h
e
Ka
p
p
a
S
ta
ti
stic
in
Re
li
a
b
il
it
y
S
tu
d
ies
:
Us
e
,
In
terp
re
tati
o
n
,
a
n
d
S
a
m
p
le
S
ize
Re
q
u
irem
e
n
ts,”
Ph
y
s.
T
h
e
r
.
,
v
o
l.
8
5
,
n
o
.
3
,
p
p
.
2
5
7
–
6
8
,
M
a
r.
2
0
0
5
.
[1
5
]
J.
R.
Lan
d
is an
d
G
.
G
.
Ko
c
h
,
“
T
h
e
M
e
a
su
re
m
e
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