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Lev
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c
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o
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mprehens
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replicatio
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c
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o
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in
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ifi
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m
o
d
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li
n
g
lan
g
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a
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(UM
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n
m
o
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re
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c
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ich
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n
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S
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stu
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ts
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t
Ei
n
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Un
iv
e
rsity
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Tec
h
n
o
lo
g
y
.
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i
n
g
th
e
sa
m
e
UML
m
o
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e
l
a
n
d
e
x
p
e
rime
n
tal
d
e
sig
n
,
we
c
o
n
d
u
c
ted
th
e
stu
d
y
with
2
3
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S
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m
p
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ter
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ien
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ts
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t
Bin
a
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sa
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tara
Un
iv
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rsity
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In
d
o
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o
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e
ffe
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t
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y
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a
n
d
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o
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sta
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n
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o
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iffere
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ts’
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a
c
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rt
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ial
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o
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th
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g
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e
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ifi
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ll
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le
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iew
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m
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l
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ra
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l
y
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a
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rt
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larly
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o
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tex
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o
l
v
in
g
p
r
o
fe
ss
io
n
a
l
so
ftwa
re
e
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g
in
e
e
rs.
K
ey
w
o
r
d
s
:
L
ev
el
o
f
d
etail
Mo
d
el
co
m
p
r
eh
en
s
io
n
ex
p
er
im
en
t
Mo
d
el
q
u
ality
UM
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m
o
d
el
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Ar
iad
i N
u
g
r
o
h
o
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
,
B
I
NUS
Gr
ad
u
ate
Pro
g
r
a
m
,
B
in
a
Nu
s
an
tar
a
Un
iv
er
s
ity
J
ak
ar
ta,
I
n
d
o
n
esia
E
m
ail:
ar
iad
i.n
u
g
r
o
h
o
@
b
in
u
s
.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
u
n
if
ied
m
o
d
elin
g
lan
g
u
ag
e
(
UM
L
)
is
a
s
tan
d
ar
d
n
o
tatio
n
wid
ely
ad
o
p
te
d
f
o
r
v
is
u
alizin
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s
p
ec
if
y
in
g
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a
n
d
d
o
c
u
m
en
tin
g
s
o
f
twar
e
s
y
s
tem
s
.
UM
L
p
la
y
s
a
cr
itical
r
o
le
in
b
o
th
s
o
f
twar
e
en
g
in
ee
r
in
g
r
esear
ch
an
d
i
n
d
u
s
tr
ial
p
r
ac
tic
e.
Pre
v
io
u
s
s
tu
d
ies
,
f
o
r
ex
am
p
le
in
[
1]
,
[
2
]
h
av
e
h
i
g
h
lig
h
te
d
d
iv
er
s
e
m
o
d
elin
g
s
ty
les
an
d
lev
els
o
f
r
ig
o
r
i
n
u
s
in
g
UM
L
;
h
o
we
v
er
,
f
e
w
h
av
e
ex
am
in
e
d
h
o
w
th
ese
v
ar
iatio
n
s
im
p
ac
t
d
ev
elo
p
m
e
n
t
o
u
tco
m
es
s
u
ch
a
s
m
o
d
el
c
o
m
p
r
e
h
en
s
io
n
.
Ou
r
p
r
ev
io
u
s
s
tu
d
y
[
3
]
,
in
v
o
lv
in
g
5
3
MSc
s
tu
d
e
n
ts
at
E
in
d
h
o
v
en
Un
iv
e
r
s
ity
o
f
T
ec
h
n
o
lo
g
y
,
d
e
m
o
n
s
tr
ated
th
at
a
h
ig
h
er
lev
el
o
f
d
etail
(
L
o
D
)
in
UM
L
class
an
d
s
eq
u
en
ce
d
iag
r
am
s
s
ig
n
if
ican
t
ly
im
p
r
o
v
es
m
o
d
el
c
o
m
p
r
e
h
e
n
s
io
n
,
b
o
t
h
in
ter
m
s
o
f
co
r
r
ec
t
n
ess
(
p
er
ce
n
tag
e
o
f
co
r
r
ec
t
an
s
wer
s
)
an
d
e
f
f
icien
c
y
(
co
r
r
ec
t
an
s
wer
s
p
er
u
n
it
ti
m
e)
.
T
h
is
r
ep
licatio
n
s
tu
d
y
ai
m
s
to
v
alid
ate
th
o
s
e
f
in
d
in
g
s
b
y
r
ep
ea
tin
g
th
e
e
x
p
e
r
im
en
t w
ith
2
3
MSc
s
tu
d
e
n
ts
f
r
o
m
B
in
a
Nu
s
an
tar
a
Un
iv
e
r
s
ity
,
I
n
d
o
n
esia.
I
n
p
r
ac
tice,
t
h
e
v
ar
iatio
n
in
UM
L
m
o
d
elin
g
r
ig
o
r
m
an
if
es
ts
in
d
if
f
er
in
g
d
eg
r
ee
s
o
f
co
m
p
leten
ess
,
g
r
an
u
lar
ity
,
an
d
p
r
o
p
o
r
tio
n
w
ith
in
m
o
d
els.
Desp
ite
its
r
ele
v
an
ce
,
th
e
ef
f
ec
t
o
f
m
o
d
elin
g
s
ty
le
,
p
ar
ticu
lar
ly
L
o
D
,
o
n
m
o
d
el
co
m
p
r
eh
e
n
s
io
n
r
em
ain
s
u
n
d
er
ex
p
lo
r
e
d
.
I
n
o
u
r
p
r
ev
i
o
u
s
s
tu
d
y
[
3
]
,
we
r
e
v
iewe
d
ea
r
ly
wo
r
k
s
o
n
UM
L
v
i
s
u
aliza
tio
n
(
e.
g
.
,
P
u
r
ch
ase
et
al.
o
n
class
d
iag
r
a
m
n
o
tatio
n
s
[
1
]
)
,
co
m
p
ar
ativ
e
an
aly
s
es
o
f
d
iag
r
am
ty
p
es (
e.
g
.
,
Oter
o
an
d
Do
lad
o
o
n
s
eq
u
en
ce
v
s
.
co
llab
o
r
atio
n
d
iag
r
am
s
[
2
]
)
,
an
d
m
o
d
elin
g
r
i
g
o
r
(
e.
g
.
,
B
r
ian
d
et
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
4
1
,
No
.
3
,
Ma
r
ch
20
2
6
:
1
0
9
5
-
1
1
0
4
1096
al.
o
n
OC
L
co
n
s
tr
ain
ts
[
4
]
)
.
At
th
e
tim
e,
th
e
r
o
le
o
f
L
o
D
in
UM
L
d
ia
g
r
am
s
-
th
at
is
,
as
a
p
o
te
n
tial
f
ac
to
r
af
f
ec
tin
g
m
o
d
el
co
m
p
r
eh
e
n
s
io
n
,
was
s
till
lar
g
ely
u
n
ex
p
l
o
r
ed
.
Sin
ce
2
0
0
9
,
h
o
wev
e
r
,
r
esear
ch
o
n
UM
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co
m
p
r
eh
e
n
s
io
n
h
as
ev
o
l
v
ed
s
ig
n
if
ican
tly
.
R
ec
en
t
wo
r
k
i
n
th
is
ar
ea
ca
n
b
e
ca
teg
o
r
ize
d
in
to
th
r
ee
r
e
s
ea
r
ch
ar
ea
s
:
(
1
)
th
e
im
p
ac
t
o
f
th
e
le
v
el
o
f
d
etail
in
UM
L
d
iag
r
am
s
,
(
2
)
th
e
im
p
ac
t
o
f
u
s
in
g
d
if
f
er
en
t
UM
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d
iag
r
a
m
ty
p
es,
an
d
(
3
)
th
e
im
p
ac
t o
f
d
i
ag
r
am
lay
o
u
t a
n
d
v
is
u
aliza
tio
n
tech
n
iq
u
es.
Fer
n
a
n
d
e
z
-
S
ae
z
et
a
l.
[
5
]
i
n
v
e
s
tig
ate
d
h
o
w
L
o
D
in
u
s
e
c
ase
,
s
e
q
u
e
n
c
e,
a
n
d
cl
ass
d
i
ag
r
a
m
s
in
f
l
u
e
n
c
es
s
o
f
twa
r
e
m
a
in
te
n
a
n
c
e.
Usi
n
g
tw
o
J
av
a
s
y
s
te
m
s
a
n
d
1
1
s
t
u
d
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p
a
r
ti
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a
n
ts
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h
e
y
f
o
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n
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a
s
l
ig
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d
e
n
c
y
to
w
ar
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b
ett
er
r
es
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lts
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h
lo
w
-
L
o
D
d
ia
g
r
a
m
s
.
H
o
we
v
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u
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to
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s
m
all
s
a
m
p
le
s
iz
e
,
t
h
es
e
r
es
u
l
ts
w
er
e
co
n
s
i
d
e
r
e
d
p
r
eli
m
i
n
a
r
y
.
A
s
e
r
i
es o
f
r
e
lat
e
d
ex
p
er
im
en
ts
b
y
S
ca
n
n
iel
lo
e
t a
l
.
f
o
c
u
s
ed
o
n
h
o
w
U
ML
a
n
a
ly
s
is
a
n
d
d
esi
g
n
m
o
d
els i
n
f
l
u
e
n
c
e
c
o
d
e
co
m
p
r
eh
e
n
s
i
o
n
a
n
d
m
o
d
if
ica
ti
o
n
tas
k
s
[
6
]
-
[
9
]
.
Ac
r
o
s
s
1
2
co
n
tr
o
ll
ed
e
x
p
er
im
e
n
ts
wit
h
p
ar
t
ici
p
an
ts
o
f
v
a
r
y
in
g
e
x
p
e
r
tis
e,
th
ey
f
o
u
n
d
t
h
at
an
al
y
s
is
-
p
h
as
e
UM
L
m
o
d
els
m
a
y
h
i
n
d
e
r
c
o
d
e
co
m
p
r
eh
e
n
s
i
o
n
a
n
d
in
cr
ea
s
e
t
ask
c
o
m
p
l
eti
o
n
ti
m
e
,
w
h
i
l
e
d
esi
g
n
-
p
h
ase
m
o
d
els
im
p
r
o
v
e
c
o
m
p
r
eh
e
n
s
i
o
n
o
u
tc
o
m
es
.
A
n
o
t
h
e
r
d
im
en
s
io
n
o
f
L
o
D
i
n
v
o
l
v
es
t
h
e
u
s
e
o
f
U
ML
s
te
r
e
o
t
y
p
es
.
C
r
u
z
-
L
em
u
s
et
a
l
.
[
1
0
]
ev
al
u
at
e
d
th
e
i
m
p
ac
t
o
f
s
t
er
eo
ty
p
es
o
n
s
eq
u
e
n
ce
d
i
ag
r
a
m
co
m
p
r
e
h
e
n
s
i
o
n
,
u
s
i
n
g
t
h
e
c
o
g
n
i
ti
v
e
t
h
eo
r
y
o
f
m
u
lti
m
e
d
i
a
lea
r
n
i
n
g
(
C
T
ML
)
as
a
t
h
e
o
r
eti
ca
l
b
as
is
.
T
h
ei
r
e
x
p
e
r
i
m
e
n
ts
r
e
v
e
ale
d
t
h
a
t
s
te
r
e
o
t
y
p
es
s
ig
n
i
f
i
ca
n
tl
y
a
id
s
em
an
tic
co
m
p
r
eh
e
n
s
i
o
n
a
n
d
r
et
e
n
ti
o
n
,
esp
ec
i
all
y
am
o
n
g
d
o
m
ai
n
n
o
v
i
ce
s
.
R
ic
ca
et
a
l.
[
1
1
]
f
o
u
n
d
th
a
t
w
h
il
e
s
t
er
e
o
t
y
p
es
d
o
n
o
t
g
e
n
er
all
y
im
p
r
o
v
e
co
m
p
r
e
h
e
n
s
i
o
n
,
t
h
e
y
h
el
p
r
e
d
u
ce
t
h
e
p
e
r
f
o
r
m
an
ce
g
a
p
b
e
twe
en
l
ess
e
x
p
e
r
i
en
ce
d
a
n
d
m
o
r
e
e
x
p
e
r
i
en
ce
d
d
e
v
el
o
p
e
r
s
.
Simil
a
r
i
n
v
es
ti
g
at
io
n
s
o
n
UM
L
s
t
er
e
o
t
y
p
es
in
cl
u
d
e
w
o
r
k
s
b
y
[
1
2
]
-
[
1
4
]
.
S
e
v
e
r
a
l
s
t
u
d
i
e
s
h
a
v
e
a
s
s
e
s
s
e
d
w
h
e
t
h
e
r
s
p
e
c
i
f
i
c
t
y
p
e
s
o
f
U
M
L
d
i
a
g
r
a
m
s
a
f
f
e
c
t
m
o
d
e
l
c
o
m
p
r
e
h
e
n
s
i
o
n
.
T
o
r
c
h
i
a
n
o
e
t
a
l
.
[
9
]
e
x
a
m
i
n
e
d
t
h
e
b
e
n
e
f
i
t
o
f
a
d
d
i
n
g
U
M
L
o
b
j
e
c
t
d
i
a
g
r
a
m
s
t
o
c
l
a
s
s
d
i
a
g
r
a
m
s
.
I
n
a
f
a
m
i
l
y
o
f
f
o
u
r
c
o
n
t
r
o
l
l
e
d
e
x
p
e
r
i
m
e
n
t
s
w
i
t
h
u
n
d
e
r
g
r
a
d
u
a
t
e
a
n
d
g
r
a
d
u
a
t
e
s
t
u
d
e
n
t
s
,
t
h
e
y
f
o
u
n
d
t
h
a
t
o
b
j
e
c
t
d
i
a
g
r
a
m
s
i
m
p
r
o
v
e
d
c
o
m
p
r
e
h
e
n
s
i
o
n
o
n
l
y
a
m
o
n
g
m
o
r
e
e
x
p
e
r
i
e
n
c
e
d
p
a
r
t
i
c
i
p
a
n
t
s
,
w
i
t
h
n
o
m
e
a
s
u
r
a
b
l
e
t
i
m
e
a
d
v
a
n
t
a
g
e
.
F
e
l
d
e
r
e
r
e
t
a
l
.
c
o
m
p
a
r
e
d
a
c
t
i
v
i
t
y
d
i
a
g
r
a
m
s
a
n
d
s
t
a
t
e
m
a
c
h
i
n
e
s
i
n
t
h
e
c
o
n
t
e
x
t
o
f
t
e
s
t
c
a
s
e
d
e
r
i
v
a
t
i
o
n
[
1
5
]
.
T
h
e
i
r
e
x
p
e
r
i
m
e
n
t
w
i
t
h
8
4
s
t
u
d
e
n
t
s
s
h
o
w
e
d
t
h
a
t
w
h
i
l
e
a
c
t
i
v
i
t
y
d
i
a
g
r
a
m
s
w
e
r
e
m
o
r
e
c
o
m
p
r
e
h
e
n
s
i
b
l
e
,
t
h
e
y
l
e
d
t
o
m
o
r
e
e
r
r
o
r
s
.
T
h
e
f
i
n
d
i
n
g
s
s
u
g
g
e
s
t
t
h
a
t
m
o
d
e
l
u
n
d
e
r
s
t
a
n
d
i
n
g
a
n
d
e
r
r
o
r
-
p
r
o
n
e
b
e
h
a
v
i
o
u
r
i
n
t
e
s
t
d
e
s
i
g
n
a
r
e
n
o
t
n
e
c
e
s
s
a
r
i
l
y
c
o
r
r
e
l
a
t
e
d
.
S
i
m
i
l
a
r
l
y
,
A
b
r
a
h
a
o
e
t
a
l
.
c
o
n
d
u
c
t
e
d
f
i
v
e
e
x
p
e
r
i
m
e
n
t
s
i
n
v
o
l
v
i
n
g
1
1
2
p
a
r
t
i
c
i
p
a
n
t
s
(
s
t
u
d
e
n
t
s
a
n
d
p
r
o
f
e
s
s
i
o
n
a
l
s
)
t
o
a
s
s
e
s
s
t
h
e
i
m
p
a
c
t
o
f
U
M
L
s
e
q
u
e
n
c
e
d
i
a
g
r
a
m
s
o
n
u
n
d
e
r
s
t
a
n
d
i
n
g
f
u
n
c
t
i
o
n
a
l
r
e
q
u
i
r
e
m
e
n
t
s
.
R
e
s
u
l
t
s
i
n
d
i
c
a
t
e
d
t
h
a
t
s
e
q
u
e
n
c
e
d
i
a
g
r
a
m
s
s
i
g
n
i
f
i
c
a
n
t
l
y
e
n
h
a
n
c
e
c
o
m
p
r
e
h
e
n
s
i
o
n
f
o
r
h
i
g
h
-
a
b
i
l
i
t
y
a
n
d
e
x
p
e
r
i
e
n
c
e
d
u
s
e
r
s
[
1
6
]
.
An
o
th
er
cr
itical
f
ac
to
r
in
UM
L
co
m
p
r
eh
e
n
s
io
n
is
d
iag
r
am
lay
o
u
t.
E
x
am
i
n
ed
h
o
w
d
ia
g
r
a
m
s
ize
,
u
s
ed
as
a
p
r
o
x
y
f
o
r
lay
o
u
t
c
o
m
p
le
x
ity
,
af
f
ec
ts
u
n
d
er
s
tan
d
in
g
[
1
7
]
.
T
h
e
f
in
d
in
g
s
s
h
o
wed
a
n
eg
ativ
e
co
r
r
elatio
n
b
etwe
en
d
iag
r
am
s
ize
an
d
p
er
f
o
r
m
an
ce
,
lead
i
n
g
to
g
u
id
elin
e
s
r
ec
o
m
m
en
d
in
g
2
0
–
6
0
elem
e
n
ts
p
er
d
iag
r
am
f
o
r
o
p
tim
al
co
m
p
r
e
h
en
s
io
n
.
Sh
ar
if
et
a
l.
[
1
8
]
ex
te
n
d
ed
th
is
wo
r
k
b
y
e
v
alu
atin
g
d
if
f
er
en
t
lay
o
u
t
s
tr
ateg
ies
in
UM
L
class
d
iag
r
am
s
.
T
h
ey
f
o
u
n
d
th
at
m
u
lti
-
clu
s
ter
la
y
o
u
ts
im
p
r
o
v
e
co
m
p
r
eh
e
n
s
io
n
ac
cu
r
ac
y
,
r
ed
u
c
e
co
m
p
letio
n
tim
e,
a
n
d
lo
we
r
v
i
s
u
al
ef
f
o
r
t,
esp
ec
ially
f
o
r
co
m
p
lex
m
o
d
elin
g
task
s
.
W
h
ile
th
er
e
h
av
e
b
ee
n
m
an
y
s
tu
d
ies
co
n
d
u
cted
to
ev
al
u
ate
th
e
im
p
ac
t
o
f
d
if
f
e
r
en
t
s
ty
les
o
f
u
s
in
g
UM
L
o
n
s
o
f
twar
e
d
ev
elo
p
m
e
n
t
an
d
m
ain
ten
an
ce
,
we
ar
g
u
e
th
at
th
e
r
esu
lts
ar
e
f
a
r
f
r
o
m
c
o
n
clu
s
iv
e.
T
h
er
e
f
o
r
e
in
th
is
s
tu
d
y
we
ex
ten
d
p
r
ev
io
u
s
r
esear
ch
b
y
r
e
p
licatin
g
a
co
n
tr
o
lled
e
x
p
er
im
e
n
t o
n
L
o
D
o
r
ig
in
ally
c
o
n
d
u
cted
in
[
3
]
.
Similar
to
t
h
e
o
r
ig
i
n
al
s
tu
d
y
,
L
o
D
is
d
ef
in
e
d
as
t
h
e
am
o
u
n
t
o
f
in
f
o
r
m
atio
n
u
s
ed
to
r
ep
r
esen
t
UM
L
m
o
d
elin
g
elem
e
n
ts
.
Fo
r
s
eq
u
en
ce
d
iag
r
a
m
s
,
a
m
ess
ag
e
co
u
ld
b
e
a
n
in
f
o
r
m
al
lab
el,
a
m
eth
o
d
n
am
e,
o
r
a
m
eth
o
d
with
p
ar
a
m
eter
s
.
Fo
r
class
d
iag
r
am
s
,
L
o
D
in
clu
d
e
s
class
attr
ib
u
tes,
o
p
er
atio
n
s
,
ass
o
ciatio
n
n
am
es,
d
ir
ec
tio
n
ality
,
an
d
m
u
ltip
licity
.
L
o
w
L
o
D
u
s
es m
in
im
al
elem
en
ts
(
e.
g
.
,
class
n
am
es,
b
asic a
s
s
o
ciatio
n
s
)
,
wh
ile
h
ig
h
L
o
D
ad
d
s
d
etailed
s
p
ec
if
icatio
n
s
.
B
y
co
n
d
u
ctin
g
th
is
r
e
p
licatio
n
ex
p
er
im
en
t,
we
aim
t
o
v
alid
ate
f
in
d
in
g
s
in
o
u
r
o
r
ig
in
al
s
tu
d
y
in
a
d
if
f
e
r
en
t e
x
p
e
r
im
en
tal
co
n
te
x
t
.
R
ep
licatio
n
s
er
v
es
a
cr
u
cial
r
o
le
in
s
tr
en
g
t
h
en
in
g
em
p
ir
ical
e
v
id
en
ce
b
y
v
alid
atin
g
p
r
e
v
io
u
s
f
in
d
in
g
s
ac
r
o
s
s
d
if
f
er
en
t
co
n
tex
ts
an
d
co
n
d
itio
n
s
[
1
9
]
.
Un
f
o
r
tu
n
ately
,
r
ep
licatio
n
is
r
elativ
ely
r
ar
e
in
s
o
f
twar
e
en
g
in
ee
r
in
g
r
esear
c
h
[
2
0
]
.
B
y
co
n
d
u
ctin
g
th
is
s
tu
d
y
,
we
ai
m
to
c
o
n
tr
ib
u
te
in
ad
d
r
ess
in
g
th
e
well
-
r
ec
o
g
n
ized
s
ca
r
city
o
f
r
ep
licatio
n
s
tu
d
ie
s
in
s
o
f
twar
e
en
g
in
ee
r
in
g
r
e
s
ea
r
ch
,
wh
ich
ar
e
ess
en
tial
f
o
r
v
alid
atin
g
th
e
g
en
er
aliza
b
ilit
y
a
n
d
r
o
b
u
s
tn
ess
o
f
e
m
p
ir
ical
f
in
d
in
g
s
.
Nev
e
r
th
eless
,
a
s
u
cc
ess
f
u
l
r
e
p
licatio
n
d
o
es
n
o
t
im
p
ly
id
en
tical
o
u
tco
m
es;
ev
en
d
if
f
er
in
g
r
esu
lts
ca
n
y
ield
v
alu
ab
le
in
s
ig
h
ts
a
n
d
co
n
tr
ib
u
te
to
a
d
ee
p
e
r
u
n
d
er
s
tan
d
i
n
g
o
f
th
e
s
tu
d
ied
p
h
en
o
m
e
n
a.
2.
M
E
T
H
O
D
T
h
is
s
tu
d
y
in
v
o
lv
ed
2
3
MSc
s
tu
d
en
ts
in
C
o
m
p
u
ter
Scien
ce
at
B
in
a
Nu
s
an
t
ar
a
Un
iv
e
r
s
ity
,
J
ak
ar
ta,
I
n
d
o
n
esia,
in
2
0
2
4
.
T
h
e
ex
p
er
im
en
t
was
a
m
an
d
ato
r
y
ass
ig
n
m
en
t
with
ad
ju
s
ted
g
r
ad
in
g
to
ac
co
u
n
t
f
o
r
L
o
D
tr
ea
tm
en
ts
.
Su
b
ject
s
h
ad
b
as
ic
UM
L
tr
ain
in
g
th
r
o
u
g
h
co
u
r
s
ewo
r
k
,
co
m
p
ar
a
b
le
to
th
e
o
r
ig
in
al
s
u
b
jects.
Fo
llo
win
g
th
e
d
ef
in
itio
n
in
[
1
9
]
,
th
is
p
ap
er
p
r
esen
ts
an
ex
ac
t
an
d
d
ep
en
d
en
t
r
ep
licatio
n
.
A
n
ex
ac
t
r
ep
licatio
n
attem
p
ts
to
f
o
llo
w
th
e
o
r
ig
in
al
s
tu
d
y
’
s
p
r
o
ce
d
u
r
es a
s
clo
s
ely
as p
o
s
s
ib
le,
wh
ile
a
d
ep
en
d
en
t r
ep
licatio
n
r
etain
s
th
e
s
am
e
o
r
s
im
ilar
ex
p
e
r
im
en
tal
co
n
d
itio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Leve
l o
f d
eta
il in
UML mo
d
els a
n
d
its
imp
a
ct
o
n
mo
d
el
c
o
mp
r
eh
en
s
io
n
…
(
A
r
ia
d
i Nu
g
r
o
h
o
)
1097
2
.
1
.
Va
ri
a
bles
in t
he
ex
pe
ri
m
ent
I
d
en
tical
to
th
e
o
r
i
g
in
al
e
x
p
e
r
im
en
t,
th
e
in
d
ep
en
d
en
t
v
ar
ia
b
le
was
L
o
D
(
lo
w
L
o
D
v
s
.
h
ig
h
L
o
D)
,
m
an
ip
u
lated
b
y
v
ar
y
in
g
in
f
o
r
m
atio
n
in
UM
L
class
an
d
s
eq
u
en
ce
d
ia
g
r
am
s
.
T
h
e
d
ep
en
d
en
t
v
ar
iab
le
in
t
h
e
ex
p
er
im
en
t
was
m
o
d
el
co
m
p
r
eh
en
s
i
o
n
.
Mo
d
el
co
m
p
r
eh
e
n
s
io
n
was
d
ef
in
ed
as
th
e
ab
ilit
y
o
f
th
e
s
u
b
jects
to
u
n
d
er
s
tan
d
co
n
ce
p
ts
/co
n
s
tr
u
cts
d
escr
ib
ed
in
a
UM
L
m
o
d
el.
W
e
d
ef
in
e
d
two
asp
ec
ts
o
f
m
o
d
el
co
m
p
r
eh
e
n
s
io
n
,
n
am
ely
co
m
p
r
eh
en
s
io
n
c
o
r
r
ec
tn
ess
an
d
c
o
m
p
r
eh
en
s
io
n
ef
f
icien
c
y
:
−
C
o
m
p
r
eh
en
s
io
n
c
o
r
r
e
ctn
ess
: Per
ce
n
tag
e
o
f
co
r
r
ec
t a
n
s
wer
s
to
a
1
5
-
q
u
esti
o
n
q
u
esti
o
n
n
air
e
.
−
C
o
m
p
r
eh
en
s
io
n
f
f
icien
cy
: N
u
m
b
er
o
f
co
r
r
ec
t a
n
s
wer
s
d
iv
id
ed
b
y
t
o
tal
tim
e
s
p
en
t.
B
o
th
co
r
r
ec
tn
ess
an
d
ef
f
icien
cy
wer
e
m
ea
s
u
r
ed
i
n
a
r
atio
s
ca
le.
W
e
u
s
e
th
e
ter
m
co
m
p
r
e
h
en
s
io
n
c
o
r
r
ec
tn
ess
an
d
co
m
p
r
eh
e
n
s
io
n
ef
f
icien
c
y
to
r
ef
er
t
o
co
r
r
ec
tn
ess
an
d
ef
f
i
cien
cy
,
r
esp
ec
tiv
ely
.
2
.
2
.
H
y
po
t
hes
es
f
o
rm
ula
t
io
n
I
n
r
e
p
licatin
g
th
e
o
r
i
g
in
al
s
t
u
d
y
o
n
th
e
r
o
le
o
f
L
o
D
in
UM
L
m
o
d
els,
we
ad
o
p
ted
th
e
s
am
e
h
y
p
o
th
eses
to
ex
am
in
e
its
im
p
ac
t o
n
m
o
d
el
c
o
m
p
r
eh
en
s
io
n
.
Hy
p
o
th
esis
1
(
C
o
m
p
r
e
h
en
s
io
n
C
o
r
r
ec
tn
ess
)
−
H1
,
n
u
ll:
T
h
er
e
is
n
o
s
ig
n
if
ica
n
t
d
if
f
er
en
ce
i
n
co
m
p
r
eh
en
s
io
n
co
r
r
ec
tn
ess
b
etwe
en
s
u
b
ject
s
wo
r
k
in
g
with
UM
L
d
iag
r
am
s
m
o
d
ele
d
with
h
ig
h
v
e
r
s
u
s
lo
w
L
o
D.
−
H1
,
alt:
T
h
e
u
s
e
o
f
UM
L
d
i
ag
r
am
s
with
h
ig
h
L
o
D
s
ig
n
i
f
ican
tly
im
p
r
o
v
es
s
u
b
ject
s
’
co
m
p
r
eh
e
n
s
io
n
co
r
r
ec
tn
ess
.
Hy
p
o
th
esis
2
(
C
o
m
p
r
e
h
en
s
io
n
E
f
f
icien
cy
)
−
H2
,
n
u
ll:
T
h
e
r
e
is
n
o
s
ig
n
if
ica
n
t
d
if
f
e
r
en
ce
i
n
co
m
p
r
e
h
en
s
io
n
ef
f
icien
cy
b
etwe
en
s
u
b
ject
s
wo
r
k
in
g
with
UM
L
d
iag
r
am
s
m
o
d
ele
d
with
h
ig
h
v
e
r
s
u
s
lo
w
L
o
D.
−
H2
,
alt:
T
h
e
u
s
e
o
f
UM
L
d
i
ag
r
am
s
with
h
ig
h
L
o
D
s
ig
n
if
ican
tly
im
p
r
o
v
es
s
u
b
ject
s
’
co
m
p
r
eh
e
n
s
io
n
ef
f
icien
cy
.
No
te
th
at
we
s
tated
o
n
e
-
tailed
h
y
p
o
th
eses
b
ec
a
u
s
e
we
h
ad
p
r
i
o
r
p
r
ed
ictio
n
s
th
at
L
o
D
in
UM
L
d
iag
r
am
s
will in
cr
ea
s
e
b
o
th
c
o
m
p
r
eh
en
s
io
n
co
r
r
ec
tn
ess
an
d
co
m
p
r
eh
e
n
s
io
n
ef
f
icien
cy
.
2
.
3
.
E
x
perim
ent
i
ns
t
rum
ent
s
T
h
is
s
ec
tio
n
p
r
esen
ts
th
e
m
ater
ials
u
s
ed
in
th
e
r
ep
licatio
n
ex
p
er
im
en
t.
W
e
b
eg
in
b
y
d
es
cr
ib
in
g
th
e
UM
L
m
o
d
el
ar
tifa
cts,
f
o
llo
w
ed
b
y
th
e
m
o
d
el
co
m
p
r
eh
e
n
s
io
n
q
u
esti
o
n
n
air
e,
b
ac
k
g
r
o
u
n
d
q
u
esti
o
n
n
air
e,
an
d
f
ee
d
b
ac
k
q
u
esti
o
n
n
air
e
.
All m
ater
ials
u
s
ed
in
th
is
r
ep
licatio
n
wer
e
b
ased
o
n
th
o
s
e
f
r
o
m
th
e
o
r
ig
in
al
s
tu
d
y
.
2
.
3
.
1
.
T
he
UM
L
m
o
del
T
h
e
s
u
b
ject
o
f
th
e
r
ep
licatio
n
ex
p
e
r
im
en
t
was
a
UM
L
m
o
d
el
r
ep
r
esen
tin
g
a
lib
r
a
r
y
s
y
s
tem
,
o
r
ig
in
ally
a
d
ap
ted
f
r
o
m
th
e
m
o
d
el
d
esc
r
ib
e
d
in
[
2
1
]
.
E
ac
h
s
u
b
ject
was
p
r
o
v
id
ed
with
a
d
o
cu
m
en
t
co
n
tain
in
g
th
e
UM
L
m
o
d
el
.
T
o
ex
am
in
e
th
e
ef
f
ec
ts
o
f
L
o
D
o
n
m
o
d
el
co
m
p
r
eh
e
n
s
io
n
,
two
v
er
s
io
n
s
o
f
th
e
UM
L
m
o
d
el
wer
e
cr
ea
ted
(
ea
ch
m
o
d
el
co
n
s
is
ted
o
f
2
3
UM
L
d
iag
r
am
s
)
,
n
am
ely
Mo
d
el
M
-
L
o
w,
wh
ich
p
r
esen
t
s
th
e
lib
r
ar
y
s
y
s
tem
with
a
lo
wer
lev
el
o
f
d
etail,
an
d
Mo
d
el
M
-
Hig
h
,
w
h
ich
p
r
esen
ts
th
e
s
am
e
s
y
s
tem
with
a
h
ig
h
er
lev
el
o
f
d
etail.
Fo
u
r
ty
p
es
o
f
UM
L
d
iag
r
am
s
wer
e
u
s
ed
in
th
e
e
x
p
er
im
en
t.
H
o
we
v
er
,
th
e
ex
p
er
im
en
tal
tr
ea
tm
en
ts
wer
e
o
n
ly
ap
p
lied
to
class
an
d
s
eq
u
en
ce
d
iag
r
am
s
.
T
h
is
d
ec
i
s
io
n
r
ef
lects th
e
f
in
d
in
g
s
in
[
2
2
]
,
wh
ich
id
en
tifie
d
class
an
d
s
eq
u
en
ce
d
iag
r
am
s
as th
e
m
o
s
t f
r
eq
u
e
n
tly
u
s
ed
U
ML
d
iag
r
am
ty
p
es in
p
r
ac
tice.
T
ab
le
1
s
u
m
m
ar
izes
th
e
L
o
D
tr
ea
tm
en
ts
ap
p
lied
in
th
e
ex
p
er
im
en
t.
Fo
r
class
d
iag
r
am
s
,
m
o
d
el
M
-
h
ig
h
i
n
clu
d
es
attr
ib
u
tes,
o
p
e
r
atio
n
s
,
an
d
lab
eled
ass
o
ciatio
n
s
,
wh
er
ea
s
m
o
d
el
M
-
lo
w
o
m
its
th
ese
d
e
tails
.
So
m
e
ass
o
ciatio
n
s
wer
e
la
b
eled
in
m
-
lo
w
wh
en
ess
en
tial
f
o
r
b
asic
s
y
s
tem
u
n
d
e
r
s
tan
d
in
g
,
to
p
r
eser
v
e
in
f
o
r
m
atio
n
s
u
f
f
icien
cy
.
Acr
o
s
s
b
o
th
v
er
s
io
n
s
,
2
0
class
es
wer
e
m
o
d
eled
.
Mo
d
el
M
-
h
ig
h
in
clu
d
ed
2
3
attr
ib
u
tes
an
d
1
2
3
o
p
e
r
atio
n
s
,
1
4
%
o
f
wh
ich
we
r
e
s
im
p
le
g
etter
m
eth
o
d
s
,
o
f
ten
p
r
esen
t
in
en
tity
cla
s
s
es.
A
lth
o
u
g
h
g
etter
s
ar
e
g
en
er
ally
tr
iv
ial,
th
e
y
wer
e
r
etain
ed
in
s
o
m
e
ca
s
es
to
s
u
p
p
o
r
t
r
ea
lis
tic
in
ter
ac
tio
n
s
in
th
e
co
r
r
esp
o
n
d
in
g
s
eq
u
en
ce
d
iag
r
am
s
.
Fo
r
s
eq
u
en
ce
d
iag
r
am
s
,
tr
ea
tm
e
n
ts
f
o
cu
s
ed
o
n
th
e
d
etail
s
o
f
m
ess
ag
e
s
.
I
n
m
-
h
ig
h
,
m
ess
ag
es
p
r
ec
is
ely
r
ef
lect
th
e
o
p
e
r
atio
n
s
d
e
f
in
ed
in
class
d
iag
r
am
s
,
in
clu
d
in
g
p
ar
am
eter
s
an
d
r
etu
r
n
v
alu
es.
I
n
c
o
n
tr
ast,
M
-
l
o
w
u
s
ed
d
u
m
m
y
m
ess
ag
es
-
th
at
is
,
s
im
p
l
e
tex
t
lab
els
with
o
u
t
p
ar
am
eter
s
o
r
r
etu
r
n
ty
p
es.
Asi
d
e
f
r
o
m
th
e
tr
ea
tm
en
ts
lis
ted
,
all
o
th
er
asp
ec
ts
(
e.
g
.
,
d
iag
r
a
m
lay
o
u
t)
wer
e
k
ep
t
co
n
s
is
ten
t
ac
r
o
s
s
b
o
th
v
er
s
io
n
s
.
Seq
u
en
ce
d
iag
r
a
m
s
wer
e
lin
k
ed
to
u
s
e
ca
s
es
to
illu
s
tr
ate
h
o
w
s
y
s
tem
f
u
n
ctio
n
ality
is
ex
ec
u
ted
v
ia
o
b
ject
in
ter
ac
tio
n
s
.
T
h
e
class
d
i
ag
r
am
s
p
r
o
v
id
ed
s
tatic
r
ep
r
ese
n
tatio
n
s
to
s
u
p
p
o
r
t
th
ese
d
y
n
am
ic
v
iews.
Fig
u
r
e
1
s
h
o
ws
an
ex
am
p
le
o
f
a
class
d
iag
r
am
r
ep
r
esen
ted
u
s
in
g
d
if
f
er
e
n
t
lev
els
o
f
d
eta
il
.
A
clas
s
d
iag
r
am
with
L
o
w
L
o
D
o
m
its
class
attr
ib
u
tes an
d
m
eth
o
d
s
.
On
th
e
o
th
er
h
an
d
,
a
class
d
iag
r
am
with
h
ig
h
L
o
D
s
p
ec
if
ies all
clas
s
attr
ib
u
tes,
m
eth
o
d
s
,
an
d
o
th
er
d
etails s
u
ch
as m
eth
o
d
s
ig
n
atu
r
es a
n
d
p
a
r
a
m
eter
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
4
1
,
No
.
3
,
Ma
r
ch
20
2
6
:
1
0
9
5
-
1
1
0
4
1098
T
ab
le
1
.
L
o
D
tr
ea
tm
en
ts
in
th
e
UM
L
m
o
d
el
t
r
ea
tm
en
ts
D
i
a
g
r
a
m
t
y
p
e
s
M
o
d
e
l
e
l
e
me
n
t
s
M
-
l
ow
M
-
h
i
g
h
#
D
i
a
g
r
a
ms
P
a
c
k
a
g
e
d
i
a
g
r
a
m
P
a
c
k
a
g
e
n
a
m
e
Y
e
s
Y
e
s
1
U
se
c
a
se
d
i
a
g
r
a
m
U
se
c
a
se
n
a
m
e
Y
e
s
Y
e
s
2
A
c
t
o
r
n
a
me
Y
e
s
Y
e
s
C
l
a
s
s
d
i
a
g
r
a
m
C
l
a
s
s a
t
t
r
i
b
u
t
e
s
No
Y
e
s
3
C
l
a
s
s
o
p
e
r
a
t
i
o
n
s
No
Y
e
s
A
sso
c
i
a
t
i
o
n
l
a
b
e
l
s
No
Y
e
s
S
e
q
u
e
n
c
e
d
i
a
g
r
a
m
R
e
a
l
m
e
t
h
o
d
n
a
mes
No
Y
e
s
17
M
e
ss
a
g
e
p
a
r
a
m
e
t
e
r
s
No
Y
e
s
M
e
ss
a
g
e
r
e
t
u
r
n
s
No
Y
e
s
(
a)
(
b
)
Fig
u
r
e
1
.
E
x
am
p
le
o
f
UM
L
cl
ass
d
iag
r
am
with
(
a)
lo
w
L
o
D
an
d
(
b
)
h
ig
h
L
o
D
2
.
3
.
2
.
M
o
del c
o
m
prehens
io
n
qu
estio
nn
a
ire
A
m
o
d
el
co
m
p
r
eh
e
n
s
io
n
q
u
e
s
tio
n
n
air
e
was
u
s
ed
to
as
s
es
s
s
u
b
ject
s
’
u
n
d
er
s
tan
d
in
g
o
f
th
e
UM
L
m
o
d
el.
T
h
e
q
u
esti
o
n
n
air
e
in
cl
u
d
ed
q
u
esti
o
n
s
s
p
ec
if
ically
r
elate
d
to
th
e
lib
r
ar
y
s
y
s
tem
an
d
r
eq
u
ir
ed
s
u
b
ject
s
to
d
er
iv
e
th
eir
a
n
s
wer
s
b
ased
s
o
lely
o
n
th
e
in
f
o
r
m
atio
n
p
r
o
v
i
d
ed
in
th
e
UM
L
s
p
ec
if
icatio
n
s
.
I
ts
s
tr
u
ctu
r
e
an
d
co
n
ten
t
wer
e
alig
n
ed
with
th
o
s
e
u
s
ed
in
th
e
o
r
ig
in
al
s
tu
d
y
t
o
m
ain
tain
co
n
s
is
ten
cy
.
T
o
m
in
im
ize
am
b
ig
u
ity
a
n
d
p
o
ten
tial
b
ias,
we
f
o
llo
wed
q
u
esti
o
n
n
air
e
d
esig
n
g
u
id
elin
es
as
d
is
cu
s
s
ed
b
y
Op
p
en
h
eim
[
2
3
]
.
T
h
e
q
u
esti
o
n
s
h
ee
t
co
n
tain
e
d
1
5
m
u
ltip
le
-
ch
o
ice
q
u
esti
o
n
s
.
E
a
ch
q
u
esti
o
n
was
co
m
p
o
s
ed
o
f
th
r
ee
m
ain
p
ar
ts
:
(
1
)
a
d
iag
r
am
r
ef
er
en
ce
,
(
2
)
th
e
q
u
esti
o
n
an
d
o
p
tio
n
s
,
a
n
d
(
3
)
a
r
em
ar
k
s
s
ec
tio
n
.
2
.
3
.
3
.
B
a
ck
g
r
o
un
d a
nd
f
ee
db
a
ck
qu
estio
nn
a
ire
As
in
th
e
o
r
ig
in
al
ex
p
er
im
en
t,
we
em
p
lo
y
ed
th
e
s
am
e
b
ac
k
g
r
o
u
n
d
q
u
esti
o
n
n
air
e
to
ass
es
s
s
u
b
ject
s
’
p
r
io
r
k
n
o
wled
g
e
an
d
e
x
p
er
ien
ce
ac
r
o
s
s
f
o
u
r
k
ey
d
o
m
ain
s
:
o
b
ject
-
o
r
ien
ted
d
esi
g
n
,
o
b
je
ct
-
o
r
ien
ted
p
r
o
g
r
a
m
m
in
g
,
UM
L
,
an
d
f
a
m
iliar
ity
with
lib
r
ar
y
s
y
s
tem
s
.
T
h
ese
f
ac
to
r
s
wer
e
id
e
n
tifie
d
as
p
o
ten
tial
co
n
f
o
u
n
d
in
g
v
ar
iab
les
th
at
m
ig
h
t
in
f
lu
en
ce
s
u
b
ject
s
’
p
er
f
o
r
m
a
n
ce
in
th
e
m
o
d
el
c
o
m
p
r
eh
e
n
s
io
n
task
.
Su
b
ject
s
’
s
elf
-
ass
es
s
ed
k
n
o
wl
ed
g
e
an
d
ex
p
er
ien
c
e
wer
e
m
e
asu
r
ed
u
s
in
g
1
0
L
ik
er
t
-
s
ca
le
it
em
s
.
An
ev
en
-
p
o
i
n
t
L
ik
er
t
s
ca
le
was
ch
o
s
en
to
r
ed
u
ce
ce
n
tr
al
ten
d
en
c
y
b
ias
b
y
d
is
co
u
r
ag
in
g
th
e
s
elec
tio
n
o
f
a
n
eu
tr
al
m
id
d
le
p
o
in
t.
T
h
is
d
esig
n
en
co
u
r
ag
es
s
u
b
ject
s
to
m
ak
e
m
o
r
e
d
elib
er
ate
s
elf
-
ass
es
s
m
en
ts
.
I
n
ad
d
itio
n
,
s
u
b
ject
s
wer
e
ask
ed
to
co
m
p
lete
a
f
ee
d
b
ac
k
q
u
esti
o
n
n
air
e
ab
o
u
t
th
ei
r
ex
p
er
ie
n
ce
d
u
r
in
g
th
e
ex
p
er
im
e
n
t.
T
h
e
s
am
e
in
s
t
r
u
m
en
t a
s
u
s
ed
in
t
h
e
o
r
i
g
in
al
s
tu
d
y
was
em
p
lo
y
ed
.
T
h
is
q
u
e
s
tio
n
n
air
e
aim
ed
to
g
ath
er
q
u
alitativ
e
an
d
q
u
an
tita
tiv
e
f
ee
d
b
ac
k
th
at
co
u
l
d
in
f
o
r
m
f
u
r
th
er
an
al
y
s
is
an
d
p
o
ten
tial
im
p
r
o
v
em
e
n
ts
to
th
e
ex
p
er
im
e
n
tal
d
esig
n
.
Su
b
ject
s
wer
e
ask
ed
to
ev
alu
ate
th
e
UM
L
m
o
d
el
th
ey
r
ec
ei
v
ed
in
ter
m
s
o
f
its
p
er
ce
iv
ed
co
m
p
lex
ity
,
co
m
p
r
e
h
en
s
ib
ilit
y
,
co
n
s
is
ten
cy
,
d
etail
ed
n
ess
,
an
d
clar
ity
.
T
h
ey
wer
e
also
ask
ed
to
r
ate
v
ar
io
u
s
asp
ec
ts
o
f
th
e
ex
p
er
im
en
tal
s
e
tu
p
f
r
o
m
th
eir
in
d
iv
id
u
al
p
er
s
p
ec
tiv
e.
E
x
ce
p
t
f
o
r
th
e
o
p
e
n
-
en
d
e
d
co
m
m
en
t sectio
n
,
all
item
s
we
r
e
ass
ess
ed
u
s
in
g
a
L
ik
er
t scale
.
2
.
4
.
E
x
perim
ent
a
l
d
esig
n
As
in
th
e
o
r
ig
i
n
al
s
tu
d
y
,
th
is
r
ep
licatio
n
f
o
llo
wed
a
s
in
g
le
-
f
ac
to
r
d
esig
n
with
two
tr
ea
tm
e
n
ts
u
s
in
g
a
co
m
p
letely
r
an
d
o
m
ized
d
esig
n
[
2
4
]
.
E
ac
h
s
u
b
ject
was
r
an
d
o
m
ly
ass
ig
n
ed
to
o
n
e
tr
ea
tm
en
t
an
d
in
te
r
ac
ted
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Leve
l o
f d
eta
il in
UML mo
d
els a
n
d
its
imp
a
ct
o
n
mo
d
el
c
o
mp
r
eh
en
s
io
n
…
(
A
r
ia
d
i Nu
g
r
o
h
o
)
1099
with
a
s
in
g
le
o
b
ject
(
i.e
.
,
U
ML
m
o
d
el)
,
f
o
llo
win
g
a
b
et
wee
n
-
s
u
b
jects
d
esig
n
.
T
h
is
d
esig
n
ch
o
ice
was
p
r
im
ar
ily
d
r
iv
en
b
y
tim
e
c
o
n
s
tr
ain
ts
,
as
th
e
ex
p
er
im
e
n
t
h
ad
t
o
b
e
co
m
p
leted
in
a
s
in
g
le
two
-
h
o
u
r
s
ess
io
n
.
T
h
e
ex
p
er
im
en
tal
o
b
ject
was
a
U
ML
m
o
d
el
r
ep
r
esen
tin
g
a
lib
r
ar
y
s
y
s
tem
.
Su
b
jects
wer
e
r
a
n
d
o
m
ly
ass
ig
n
ed
to
o
n
e
o
f
two
tr
ea
tm
e
n
t
g
r
o
u
p
s
:
L
o
w
L
o
D
(
L
-
L
o
D)
o
r
Hig
h
L
o
D
(
H
-
L
o
D)
.
T
h
o
s
e
r
ec
eiv
i
n
g
th
e
less
d
etai
led
m
o
d
el
wer
e
p
lace
d
in
t
h
e
L
-
L
o
D
g
r
o
u
p
,
wh
ile
th
o
s
e
g
iv
e
n
a
m
o
r
e
d
etailed
m
o
d
el
wer
e
ass
ig
n
ed
to
th
e
H
-
L
o
D
g
r
o
u
p
.
B
o
th
g
r
o
u
p
s
p
a
r
ticip
a
ted
in
th
e
ex
p
e
r
im
en
t
in
th
e
s
am
e
r
o
o
m
,
with
o
u
t
an
y
p
h
y
s
ical
s
ep
ar
atio
n
o
r
v
is
ib
le
g
r
o
u
p
i
d
en
tifie
r
s
.
T
h
is
was
d
o
n
e
to
r
ed
u
ce
b
ias
an
d
a
v
o
id
aler
tin
g
s
u
b
ject
s
to
th
e
ex
is
ten
ce
o
f
m
u
ltip
le
tr
ea
tm
en
t
co
n
d
itio
n
s
.
T
o
e
n
s
u
r
e
s
tatis
tical
b
alan
ce
an
d
s
im
p
lify
th
e
s
u
b
s
eq
u
en
t
an
al
y
s
is
[
2
4
]
,
we
aim
ed
f
o
r
eq
u
al
g
r
o
u
p
s
izes.
T
h
e
f
in
al
g
r
o
u
p
s
izes w
er
e
1
1
f
o
r
L
L
o
D
a
n
d
1
2
f
o
r
H
-
L
o
D.
E
ac
h
s
u
b
ject
r
ec
eiv
e
d
two
m
ater
ials
:
(
1
)
a
UM
L
m
o
d
el
o
f
th
e
lib
r
ar
y
s
y
s
tem
(
v
ar
y
in
g
i
n
lev
el
o
f
d
etail)
,
(
2
)
a
n
o
n
lin
e
q
u
esti
o
n
n
air
e
co
n
s
is
tin
g
o
f
a
m
o
d
el
co
m
p
r
eh
en
s
io
n
q
u
esti
o
n
n
air
e,
b
ac
k
g
r
o
u
n
d
q
u
esti
o
n
n
air
e,
a
n
d
f
ee
d
b
ac
k
q
u
esti
o
n
n
air
e
(
all
ar
e
id
en
t
ical
ac
r
o
s
s
b
o
th
tr
ea
tm
en
t
g
r
o
u
p
s
)
.
T
h
e
m
o
d
el
co
m
p
r
eh
e
n
s
io
n
q
u
esti
o
n
n
air
e
was
th
e
f
ir
s
t
task
aim
ed
at
ass
es
s
in
g
s
u
b
ject
s
’
u
n
d
er
s
ta
n
d
in
g
o
f
th
e
UM
L
m
o
d
el.
Fo
llo
win
g
t
h
e
c
o
m
p
r
e
h
en
s
io
n
task
,
s
u
b
ject
s
co
m
p
let
ed
th
e
b
ac
k
g
r
o
u
n
d
q
u
esti
o
n
n
a
ir
e,
wh
ich
ass
ess
ed
th
eir
p
r
i
o
r
k
n
o
wled
g
e
a
n
d
ex
p
er
ien
ce
r
elate
d
to
o
b
ject
-
o
r
ien
ted
an
al
y
s
is
an
d
d
esig
n
,
o
b
ject
-
o
r
ien
ted
p
r
o
g
r
a
m
m
in
g
,
UM
L
,
a
n
d
lib
r
a
r
y
s
y
s
tem
s
.
L
astl
y
,
th
e
f
ee
d
b
a
ck
q
u
esti
o
n
n
air
e
c
o
llected
s
u
b
jectiv
e
im
p
r
ess
io
n
s
o
f
th
e
ex
p
er
im
en
t
f
r
o
m
ea
ch
s
u
b
ject
.
T
h
e
ex
p
er
im
en
t
was
c
o
n
d
u
ct
ed
in
a
s
in
g
le
s
ess
io
n
,
s
tar
tin
g
at
0
9
:0
0
a
n
d
last
in
g
f
o
r
9
0
m
in
u
tes.
Su
b
jects
wer
e
in
s
tr
u
cted
to
c
o
m
p
lete
all
task
s
with
in
th
is
tim
e
f
r
am
e.
Su
b
ject
s
wer
e
r
a
n
d
o
m
ly
ass
ig
n
ed
t
o
tr
ea
tm
en
t
g
r
o
u
p
s
at
t
h
e
s
tar
t.
A
b
r
ief
o
r
ie
n
tatio
n
was
p
r
o
v
id
ed
to
ex
p
lain
th
e
p
r
o
ce
d
u
r
e,
with
wr
itten
in
s
tr
u
ctio
n
s
also
in
clu
d
ed
in
th
e
m
o
d
el
co
m
p
r
eh
en
s
io
n
q
u
esti
o
n
n
air
e.
Un
lik
e
th
e
o
r
ig
in
al
p
ap
er
-
b
ased
ex
p
er
im
en
t,
th
is
r
e
p
licatio
n
u
s
ed
o
n
lin
e
q
u
esti
o
n
n
air
es
e
q
u
ip
p
e
d
with
tim
e
-
tr
ac
k
in
g
f
ea
tu
r
es.
T
h
e
UM
L
m
o
d
els
wer
e
also
p
r
o
v
id
e
d
d
i
g
itally
v
ia
an
o
n
lin
e
r
ep
o
s
ito
r
y
.
Up
o
n
co
m
p
letio
n
,
all
r
esp
o
n
s
es
wer
e
co
llected
,
p
r
ep
r
o
ce
s
s
ed
,
an
d
s
to
r
ed
in
a
s
p
r
ea
d
s
h
ee
t f
o
r
an
aly
s
is
.
2
.
5
.
Ana
ly
s
is
m
et
ho
d
T
h
e
an
aly
s
is
b
eg
an
with
m
an
u
al
p
r
ep
r
o
ce
s
s
in
g
o
f
th
e
r
aw
d
ata
co
llected
f
r
o
m
th
e
q
u
est
io
n
n
air
es.
On
ce
p
r
o
ce
s
s
ed
,
th
e
d
ata
wer
e
im
p
o
r
te
d
in
to
PS
PP
[
2
5
]
f
o
r
s
tatis
tical
an
aly
s
is
.
T
h
e
n
ex
t
s
tep
in
v
o
lv
ed
d
ata
ex
p
lo
r
atio
n
,
in
clu
d
in
g
o
u
tlier
d
etec
tio
n
an
d
ch
ec
k
in
g
ass
u
m
p
tio
n
s
f
o
r
s
tatis
tical
te
s
tin
g
.
Si
n
ce
th
e
h
y
p
o
t
h
eses
f
o
cu
s
ed
o
n
co
m
p
ar
in
g
g
r
o
u
p
p
er
f
o
r
m
an
ce
,
we
a
p
p
lied
test
s
f
o
r
d
if
f
er
e
n
ce
s
b
etwe
en
two
i
n
d
ep
en
d
en
t
g
r
o
u
p
s
:
in
d
ep
en
d
en
t
t
-
test
s
f
o
r
p
ar
a
m
etr
ic
ass
u
m
p
tio
n
s
an
d
Ma
n
n
-
W
h
itn
ey
test
s
o
th
er
wis
e.
Giv
en
th
e
h
ig
h
er
s
tatis
t
ical
p
o
wer
o
f
p
a
r
am
etr
ic
test
s
,
we
p
r
io
r
itized
th
em
wh
en
ass
u
m
p
tio
n
s
wer
e
m
et.
No
r
m
ality
an
d
h
o
m
o
g
en
eity
o
f
v
ar
ia
n
ce
wer
e
ass
es
s
ed
u
s
in
g
th
e
Sh
ap
ir
o
-
W
ilk
an
d
L
ev
e
n
e’
s
test
s
,
r
esp
ec
tiv
ely
.
I
f
n
o
r
m
ality
was
v
io
lated
,
d
ata
wer
e
n
o
r
m
a
lized
u
s
in
g
ar
ea
tr
an
s
f
o
r
m
atio
n
[
2
6
]
.
A
s
ig
n
if
ican
ce
lev
el
o
f
α
=
0
.
0
5
was
u
s
ed
f
o
r
all
h
y
p
o
th
esis
test
s
.
T
o
ass
ess
th
e
p
o
ten
tial
im
p
ac
t
o
f
s
u
b
jects’
b
ac
k
g
r
o
u
n
d
k
n
o
wled
g
e
an
d
ex
p
er
ien
ce
o
n
p
er
f
o
r
m
an
ce
,
we
co
n
d
u
cted
a
two
-
way
ANOV
A
u
s
in
g
th
e
s
am
e
s
ig
n
if
ican
ce
th
r
esh
o
ld
.
Fin
ally
,
q
u
alitativ
e
r
esp
o
n
s
es
–
th
at
is
,
s
u
b
jects’
wr
itten
ju
s
tific
atio
n
s
f
o
r
th
ei
r
an
s
wer
s
wer
e
an
aly
ze
d
m
an
u
ally
to
id
en
tif
y
p
atter
n
s
an
d
r
ea
s
o
n
in
g
d
if
f
er
e
n
ce
s
ac
r
o
s
s
g
r
o
u
p
s
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
o
ev
alu
ate
th
e
e
f
f
ec
t
o
f
L
o
D
o
n
co
m
p
r
eh
e
n
s
io
n
co
r
r
ec
tn
ess
an
d
ef
f
icien
cy
,
we
co
n
d
u
cted
a
n
in
d
ep
en
d
en
t sam
p
les t
-
test
.
T
h
e
s
u
m
m
ar
y
s
tatis
tics
ar
e
p
r
esen
ted
in
T
ab
le
2
an
d
th
e
test
r
esu
lts
ar
e
in
T
ab
le
3
.
3
.
1
.
T
esting
hy
po
t
hes
is
1
:
e
f
f
ec
t
o
f
L
o
D
o
n
c
o
m
prehens
io
n c
o
rr
ec
t
nes
s
As
s
h
o
wn
in
T
ab
le
2
,
th
e
H
-
L
o
D
g
r
o
u
p
ex
h
ib
ited
h
ig
h
er
co
m
p
r
eh
en
s
io
n
co
r
r
ec
tn
ess
th
an
th
e
L
-
L
o
D
g
r
o
u
p
.
T
h
e
m
ea
n
v
alu
e
f
o
r
H
-
L
o
D
ex
ce
ed
e
d
th
at
o
f
L
-
L
o
D,
in
d
icatin
g
a
co
n
s
is
ten
t
a
d
v
an
tag
e
f
o
r
g
r
ea
ter
d
etail.
T
o
ev
alu
ate
th
e
s
tatis
ti
ca
l
s
ig
n
if
ican
ce
o
f
th
is
d
if
f
er
e
n
ce
,
we
co
n
d
u
c
ted
an
in
d
ep
en
d
en
t
s
am
p
les
t
-
test
.
T
h
e
H
-
L
o
D
g
r
o
u
p
ac
h
iev
ed
a
m
ea
n
co
m
p
r
eh
e
n
s
io
n
co
r
r
ec
tn
ess
o
f
6
4
.
4
6
(
Std
.
er
r
o
r
m
ea
n
=
3
.
7
0
)
,
co
m
p
ar
e
d
to
5
2
.
1
3
(
Std
.
er
r
o
r
m
ea
n
=
4
.
2
1
)
f
o
r
t
h
e
L
-
L
o
D
g
r
o
u
p
.
T
h
e
t
-
test
r
esu
lts
in
T
ab
le
3
,
f
o
cu
s
in
g
o
n
th
e
r
o
w
co
m
p
r
eh
e
n
s
io
n
co
r
r
ec
tn
ess
,
c
o
n
f
ir
m
e
d
th
is
d
if
f
e
r
en
ce
to
b
e
s
tatis
tical
ly
s
ig
n
if
ican
t
(
p
=
0
.
0
2
,
o
n
e
-
tailed
)
,
in
d
icatin
g
t
h
at
th
e
o
b
s
er
v
ed
ef
f
ec
t
is
u
n
lik
ely
d
u
e
to
ch
a
n
ce
.
B
ased
o
n
t
h
ese
r
esu
lts
,
we
r
ejec
t
th
e
n
u
ll
h
y
p
o
th
esis
(
H1
,
n
u
ll)
an
d
ac
ce
p
t
th
e
alter
n
ativ
e
h
y
p
o
th
esis
(
H1
,
alt)
:
T
h
e
u
s
e
o
f
UM
L
d
iag
r
am
s
with
h
ig
h
er
L
o
D
s
ig
n
if
ican
tly
im
p
r
o
v
es c
o
m
p
r
eh
en
s
io
n
co
r
r
ec
tn
ess
.
3
.
2
.
T
esting
hy
p
o
t
hes
is
2
:
e
f
f
ec
t
o
f
L
o
D
o
n
c
o
m
prehens
io
n e
f
f
iciency
As
s
h
o
wn
in
T
a
b
le
2
th
er
e
i
s
n
o
s
u
b
s
tan
tial
d
if
f
er
en
ce
in
co
m
p
r
eh
en
s
io
n
ef
f
icien
c
y
m
ea
n
v
alu
es
b
etwe
en
th
e
H
-
L
o
D
an
d
L
-
L
o
D
g
r
o
u
p
s
.
Sp
ec
if
ically
,
th
e
H
-
L
o
D
g
r
o
u
p
ac
h
iev
ed
a
m
ea
n
ef
f
icien
cy
o
f
0
.
1
8
(
Std
.
er
r
o
r
m
ea
n
=
0
.
0
2
)
,
w
h
ile
th
e
L
-
L
o
D
g
r
o
u
p
s
co
r
ed
0
.
1
9
(
Std
.
er
r
o
r
m
ea
n
=
0
.
0
3
)
.
C
o
n
s
is
ten
t
with
th
e
ea
r
lier
an
aly
s
is
,
we
co
n
d
u
cted
a
t
-
test
t
o
test
f
o
r
s
tatis
tical
s
i
g
n
if
ican
ce
.
T
h
e
t
-
test
r
esu
lts
p
r
esen
ted
in
T
ab
le
3
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
4
1
,
No
.
3
,
Ma
r
ch
20
2
6
:
1
0
9
5
-
1
1
0
4
1100
(
s
ee
r
o
w
co
m
p
r
eh
e
n
s
io
n
ef
f
i
cien
cy
)
s
h
o
ws
th
at
t
h
e
d
if
f
er
en
ce
in
m
ea
n
s
was
n
o
t
s
tat
i
s
tically
s
ig
n
if
ican
t
(
p
=
0
.
4
0
,
o
n
e
-
tailed
)
.
B
ased
o
n
th
ese
r
esu
lts
,
we
r
ej
ec
t
th
e
s
ec
o
n
d
alter
n
ativ
e
h
y
p
o
t
h
esis
(
H2
,
alt)
an
d
ac
ce
p
t
th
e
n
u
ll
h
y
p
o
th
esis
(
H2
,
n
u
ll
)
:
T
h
er
e
is
n
o
s
ig
n
i
f
ican
t
d
if
f
er
en
ce
i
n
co
m
p
r
eh
e
n
s
io
n
ef
f
icien
cy
b
etwe
en
s
u
b
jects wo
r
k
in
g
with
UM
L
d
iag
r
am
s
m
o
d
eled
at
h
ig
h
o
r
lo
w
lev
els o
f
d
etail.
T
ab
le
2
.
g
r
o
u
p
s
tatis
tics
f
o
r
co
m
p
r
eh
en
s
io
n
co
r
r
ec
tn
ess
an
d
ef
f
icien
cy
ac
r
o
s
s
lo
w
an
d
h
ig
h
lev
el
o
f
d
etail
(
L
-
L
o
D
An
d
H
-
L
o
D)
g
r
o
u
p
s
M
e
t
r
i
c
G
r
o
u
p
N
M
e
a
n
S
t
d
.
d
e
v
.
S
t
d
.
e
r
r
o
r
mea
n
C
o
m
p
r
e
h
e
n
si
o
n
c
o
r
r
e
c
t
n
e
ss
L
-
Lo
D
11
5
2
.
1
3
1
3
.
9
5
4
.
2
1
H
-
Lo
D
12
6
4
.
4
6
1
2
.
8
2
3
.
7
0
C
o
m
p
r
e
h
e
n
si
o
n
e
f
f
i
c
i
e
n
c
y
L
-
Lo
D
11
0
.
1
9
0
.
1
1
0
.
0
3
H
-
Lo
D
12
0
.
1
8
0
.
0
6
0
.
0
2
T
ab
le
3
.
I
n
d
ep
e
n
d
en
t T
-
T
est f
o
r
co
m
p
r
eh
e
n
s
io
n
co
r
r
ec
tn
ess
an
d
ef
f
icien
c
y
M
e
t
r
i
c
s
La
v
e
n
e
’
s
t
e
st
T
-
t
e
st
s
t
a
t
i
st
i
c
s
F
S
i
g
.
t
df
S
i
g
.
(
1
-
t
a
i
l
e
d
)
M
.
D
i
f
f
.
S
E
D
i
f
f
.
9
5
%
C
I
D
i
f
f
.
Lo
w
e
r
U
p
p
e
r
C
o
m
p
r
.
c
o
r
r
e
c
t
n
e
ss
0
.
6
7
0
.
4
2
-
2
.
2
1
2
1
.
0
0
0
.
0
2
1
2
.
3
3
5
.
5
8
-
2
3
.
9
4
-
0
.
7
3
C
o
m
p
r
.
e
f
f
i
c
i
e
n
c
y
1
.
4
9
0
.
2
3
0
.
2
5
2
1
.
0
0
0
.
4
0
0
.
0
1
0
.
0
4
-
0
.
0
7
0
.
0
9
3
.
3
.
Su
bje
ct
s
’
ba
ckg
ro
un
d kno
wledg
e
a
nd
ex
perience
Su
b
jects’
b
ac
k
g
r
o
u
n
d
k
n
o
wl
ed
g
e
an
d
ex
p
er
ien
ce
wer
e
as
s
ess
ed
th
r
o
u
g
h
a
s
u
b
ject
’
s
b
ac
k
g
r
o
u
n
d
q
u
esti
o
n
n
air
e.
T
h
e
q
u
esti
o
n
n
air
e
co
n
s
is
ted
o
f
1
0
item
s
r
ated
o
n
a
6
-
p
o
in
t
o
r
d
i
n
al
s
ca
le
(
1
:
No
.
k
n
o
wled
g
e/e
x
p
er
ien
ce
,
6
:
v
e
r
y
g
o
o
d
)
.
T
o
ass
ess
wh
eth
er
b
ac
k
g
r
o
u
n
d
d
if
f
er
en
ce
s
m
i
g
h
t
co
n
f
o
u
n
d
t
h
e
ex
p
er
im
en
tal
r
esu
lts
,
we
c
o
m
p
ar
ed
o
v
er
all
k
n
o
wled
g
e/ex
p
e
r
ien
ce
s
co
r
es
a
cr
o
s
s
g
r
o
u
p
s
.
E
ac
h
s
u
b
ject’
s
s
co
r
e
was
co
m
p
u
ted
as
th
e
s
u
m
o
f
all
q
u
esti
o
n
n
air
e
r
esp
o
n
s
es,
y
ield
in
g
a
r
an
g
e
o
f
1
0
to
6
0
.
Giv
en
th
e
o
r
d
i
n
al
n
atu
r
e
o
f
th
e
d
ata
,
th
e
Ma
n
n
-
W
h
itn
ey
U
test
was
u
s
ed
.
T
h
e
r
esu
lts
o
f
th
e
Ma
n
n
-
W
h
itn
ey
U
test
ar
e
p
r
esen
ted
in
T
ab
le
4
an
d
T
a
b
le
5
.
As
s
h
o
wn
in
T
ab
le
4
an
d
T
ab
le
5
,
t
h
er
e
was
n
o
s
tatis
tically
s
ig
n
if
ican
t
d
if
f
er
e
n
ce
in
b
ac
k
g
r
o
u
n
d
k
n
o
wled
g
e
a
n
d
e
x
p
er
ien
ce
b
etwe
en
th
e
two
g
r
o
u
p
s
(
p
=
0
.
2
6
4
)
.
Sin
ce
t
h
e
s
ig
n
if
ican
ce
lev
el
ex
ce
ed
s
th
e
co
n
v
en
tio
n
al
th
r
e
s
h
o
ld
o
f
0
.
0
5
,
we
co
n
clu
d
e
th
at
d
if
f
er
en
ce
s
in
p
r
io
r
k
n
o
wle
d
g
e
an
d
ex
p
er
ien
ce
ar
e
u
n
lik
ely
t
o
h
av
e
in
f
lu
e
n
ce
d
th
e
m
ain
o
u
tco
m
es o
f
th
is
ex
p
er
im
en
t.
T
ab
le
4
.
R
an
k
s
o
f
b
ac
k
g
r
o
u
n
d
k
n
o
w
led
g
e/e
x
p
er
ien
ce
s
co
r
es
G
r
o
u
p
N
M
e
a
n
r
a
n
k
S
u
m
o
f
r
a
n
k
s
L
-
Lo
D
11
1
0
.
3
6
1
1
4
.
0
0
H
-
Lo
D
12
1
3
.
5
0
1
6
2
.
0
0
T
ab
le
5
.
Ma
n
n
-
W
h
itn
ey
U
test
r
esu
lts
S
t
a
t
i
st
i
c
V
a
l
u
e
M
a
n
n
-
W
h
i
t
n
e
y
U
4
8
.
0
0
W
i
l
c
o
x
o
n
W
1
1
4
.
0
0
Z
-
1
.
1
2
A
sy
mp
.
S
i
g
.
(
2
-
t
a
i
l
e
d
)
0
.
2
6
4
3
.
4
.
In
-
depth
a
na
ly
s
es
3
.
4
.
1
.
P
er
-
qu
estio
n
co
m
prehens
io
n per
f
o
rm
a
nce
T
o
g
ain
a
d
ee
p
er
u
n
d
er
s
tan
d
i
n
g
o
f
h
o
w
th
e
L
o
D
tr
ea
tm
en
t
s
in
f
lu
en
ce
d
th
e
s
u
b
jects’
p
er
f
o
r
m
an
ce
–
th
at
is
,
in
ter
m
s
o
f
co
m
p
r
eh
e
n
s
io
n
co
r
r
ec
tn
ess
,
we
co
n
d
u
c
ted
a
q
u
alitativ
e
an
aly
s
is
o
f
th
eir
an
s
wer
s
to
th
e
m
o
d
el
co
m
p
r
eh
e
n
s
io
n
q
u
esti
o
n
n
air
e.
Fig
u
r
e
2
p
r
esen
ts
a
co
m
p
ar
is
o
n
o
f
co
r
r
ec
t
r
esp
o
n
s
es
ac
r
o
s
s
b
o
th
L
-
L
o
D
an
d
H
-
L
o
D
g
r
o
u
p
s
.
Ov
er
all,
s
u
b
jects
in
th
e
H
-
L
o
D
g
r
o
u
p
p
er
f
o
r
m
ed
b
etter
o
n
m
o
s
t
q
u
esti
o
n
s
.
No
tab
ly
,
f
o
r
f
iv
e
q
u
e
s
tio
n
s
,
n
am
ely
Q2
,
Q3
,
Q7
,
Q1
1
,
an
d
Q1
4
,
th
e
H
-
L
o
D
g
r
o
u
p
o
u
tp
e
r
f
o
r
m
ed
t
h
e
L
-
L
o
D
g
r
o
u
p
b
y
a
s
u
b
s
tan
tial
m
ar
g
in
(
at
least
th
r
ee
p
o
in
ts
)
.
T
h
ese
q
u
esti
o
n
s
ap
p
ea
r
t
o
b
e
k
e
y
co
n
tr
ib
u
t
o
r
s
t
o
th
e
o
v
er
all
d
if
f
e
r
en
ce
i
n
co
m
p
r
eh
en
s
io
n
s
co
r
es
b
etwe
en
th
e
two
e
x
p
er
im
e
n
t
al
co
n
d
itio
n
s
.
W
e
ex
a
m
in
ed
f
o
u
r
o
f
t
h
ese
q
u
esti
o
n
s
in
m
o
r
e
d
etail,
as
th
e
y
ex
h
ib
ited
th
e
m
o
s
t p
r
o
n
o
u
n
ce
d
p
er
f
o
r
m
an
ce
g
ap
s
.
−
Q3
:
A
co
m
m
o
n
m
is
co
n
ce
p
ti
o
n
am
o
n
g
L
-
L
o
D
s
u
b
ject
s
in
v
o
lv
ed
th
e
in
ter
p
r
etatio
n
o
f
th
e
p
s
eu
d
o
c
o
d
e
r
eser
v
atio
n
.
co
u
n
t
with
in
a
s
eq
u
en
ce
d
iag
r
am
.
Ma
n
y
ass
u
m
e
d
th
at
r
eser
v
atio
n
r
ef
er
r
e
d
to
a
class
,
wh
er
ea
s
it
ac
tu
ally
d
en
o
ted
a
co
n
c
ep
tu
al
en
tity
,
i.e
.
,
th
e
to
ta
l
n
u
m
b
e
r
o
f
r
eser
v
atio
n
s
.
Alth
o
u
g
h
th
is
m
is
u
n
d
er
s
tan
d
in
g
was
also
o
b
s
er
v
ed
am
o
n
g
s
o
m
e
H
-
L
o
D
s
u
b
ject
s
,
it
was
m
o
r
e
p
r
ev
ale
n
t
in
th
e
L
-
L
o
D
g
r
o
u
p
,
lik
ely
d
u
e
to
t
h
e
lack
o
f
co
n
tex
tu
al
cl
u
es in
th
e
lo
w
-
d
etail
d
iag
r
am
s
.
−
Q7
:
B
o
th
g
r
o
u
p
s
s
tr
u
g
g
le
d
t
o
co
r
r
ec
tly
id
en
tif
y
th
e
f
u
n
ct
io
n
o
f
co
n
tr
o
ller
class
es.
T
h
e
d
is
tr
ib
u
tio
n
o
f
an
s
wer
s
s
u
g
g
ests
lim
ited
f
am
i
liar
ity
with
th
e
m
o
d
el
-
v
iew
-
c
o
n
tr
o
ller
(
MV
C
)
d
esig
n
p
atter
n
.
Ho
wev
e
r
,
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Leve
l o
f d
eta
il in
UML mo
d
els a
n
d
its
imp
a
ct
o
n
mo
d
el
c
o
mp
r
eh
en
s
io
n
…
(
A
r
ia
d
i Nu
g
r
o
h
o
)
1101
L
-
L
o
D
g
r
o
u
p
was
s
ig
n
if
ican
tl
y
af
f
ec
ted
,
lik
ely
d
u
e
to
th
e
a
b
s
en
ce
o
f
attr
ib
u
te
an
d
o
p
er
at
io
n
in
f
o
r
m
atio
n
in
th
eir
class
d
iag
r
am
s
,
wh
ich
h
in
d
er
ed
th
eir
ab
ilit
y
to
r
ea
s
o
n
ab
o
u
t c
lass
r
o
les.
−
Q1
1
:
On
ly
a
f
ew
L
-
L
o
D
s
u
b
ject
s
co
r
r
ec
tly
id
e
n
tifie
d
th
e
class
r
esp
o
n
s
ib
le
f
o
r
in
s
tan
tiatin
g
th
e
R
eser
v
atio
n
o
b
ject
in
th
e
Ma
k
e
R
eser
v
atio
n
s
eq
u
en
ce
d
iag
r
am
.
I
n
co
n
tr
ast,
H
-
L
o
D
s
u
b
ject
s
b
en
ef
ited
f
r
o
m
ad
d
itio
n
al
v
is
u
al
cu
es
, s
p
ec
if
ically
th
e
r
etu
r
n
m
ess
ag
e
in
d
icatin
g
th
e
cr
ea
tio
n
o
f
a
R
es
er
v
atio
n
o
b
ject
b
y
th
e
T
itle c
lass
.
T
h
is
in
f
o
r
m
atio
n
was o
m
itted
in
th
e
lo
w
-
d
etail
v
er
s
io
n
.
−
Q1
4
:
L
-
L
o
D
s
u
b
ject
s
h
a
d
d
if
f
icu
lty
s
elec
tin
g
th
e
co
r
r
ec
t
p
s
eu
d
o
-
c
o
d
e
s
n
i
p
p
et
th
at
r
ep
r
esen
ts
th
e
d
eletio
n
o
f
a
b
o
o
k
titl
e
b
ased
o
n
th
e
s
eq
u
en
ce
d
iag
r
am
.
C
o
m
m
o
n
er
r
o
r
s
in
v
o
lv
ed
m
is
id
en
tify
i
n
g
th
e
r
elev
an
t
o
b
jects
o
r
in
co
r
r
ec
tly
s
eq
u
e
n
cin
g
th
e
d
eletio
n
s
tep
s
.
W
e
attr
ib
u
te
th
is
co
n
f
u
s
io
n
to
th
e
ab
s
en
ce
o
f
k
ey
d
etails in
th
e
L
-
L
o
D
d
ia
g
r
am
s
,
n
am
ely
th
e
lack
o
f
ex
p
licit m
ess
ag
es a
n
d
p
ar
am
eter
in
f
o
r
m
atio
n
.
Fig
u
r
e
2
.
Sco
r
es
o
f
a
ll q
u
esti
o
n
s
f
o
r
th
e
UM
L
m
o
d
el
q
u
esti
o
n
n
air
e
3
.
4
.
2
.
Su
bje
ct
s
’
f
ee
db
a
ck
I
n
th
e
f
ee
d
b
ac
k
q
u
esti
o
n
n
air
e
,
we
ask
ed
th
e
s
u
b
jects
to
ev
alu
ate
asp
ec
ts
o
f
th
e
UM
L
m
o
d
el
th
at
co
m
p
r
is
e
s
im
p
licity
,
co
m
p
r
e
h
en
s
ib
ilit
y
,
co
n
s
is
ten
cy
,
d
eta
iled
n
ess
,
an
d
clar
ity
.
Data
o
b
tain
ed
f
r
o
m
th
e
q
u
esti
o
n
n
air
e
is
p
r
esen
ted
in
Fig
u
r
e
3
.
T
h
e
f
i
g
u
r
e
d
is
p
lay
s
th
e
m
o
d
e
v
alu
e
o
f
all
q
u
esti
o
n
s
,
in
wh
ich
h
ig
h
e
r
v
alu
e
r
ep
r
esen
ts
b
etter
s
u
b
ject
im
p
r
ess
io
n
.
Ov
er
all,
s
u
b
jects
in
b
o
th
th
e
L
-
L
o
D
an
d
H
-
L
o
D
g
r
o
u
p
s
ev
alu
ate
d
m
o
s
t
asp
ec
ts
o
f
th
e
U
ML
m
o
d
els
f
av
o
u
r
ab
ly
–
p
ar
ticu
lar
ly
with
r
esp
ec
t
to
co
m
p
r
eh
en
s
ib
ilit
y
,
co
n
s
is
ten
cy
,
d
etailed
n
ess
,
an
d
clar
ity
.
Ho
w
ev
er
,
a
n
o
ta
b
le
ex
ce
p
tio
n
lies
in
th
e
ev
alu
atio
n
o
f
s
im
p
licity
:
s
u
b
jects
in
th
e
H
-
L
o
D
g
r
o
u
p
r
ated
th
e
m
o
d
el’
s
s
im
p
licity
les
s
f
av
o
u
r
ab
ly
th
a
n
th
o
s
e
in
th
e
L
-
L
o
D
g
r
o
u
p
.
I
n
ter
esti
n
g
ly
,
d
esp
ite
th
e
H
-
L
o
D
g
r
o
u
p
r
atin
g
t
h
eir
m
o
d
el
as
m
o
r
e
d
etailed
(
as
ex
p
ec
ted
)
,
t
h
ey
p
er
ce
iv
ed
it
a
s
less
s
im
p
le.
T
h
is
s
u
g
g
ests
th
at
th
e
in
cr
ea
s
e
in
m
o
d
el
d
etail
m
ay
c
o
m
e
at
th
e
c
o
s
t o
f
p
er
ce
iv
ed
s
im
p
licity
.
C
o
n
v
er
s
ely
,
L
-
L
o
D
s
u
b
jects
g
en
er
ally
g
a
v
e
m
o
r
e
f
av
o
u
r
ab
le
r
atin
g
s
ac
r
o
s
s
m
o
s
t
q
u
ality
d
i
m
en
s
io
n
s
,
d
esp
ite
th
eir
m
o
d
els
co
n
tain
in
g
less
in
f
o
r
m
atio
n
.
T
h
ese
r
esu
lts
r
aise
n
o
tab
le
o
b
s
er
v
atio
n
s
–
th
at
is
,
p
er
ce
iv
e
d
s
im
p
licity
ap
p
ea
r
s
to
b
e
o
n
ly
p
ar
tially
in
f
lu
e
n
ce
d
b
y
th
e
a
ctu
al
L
o
D
in
th
e
m
o
d
el.
T
h
is
p
r
o
m
p
ts
a
b
r
o
a
d
er
q
u
esti
o
n
o
f
w
h
eth
er
p
er
ce
iv
ed
m
o
d
el
co
m
p
lex
ity
is
tr
u
ly
d
e
p
en
d
en
t
o
n
th
e
am
o
u
n
t
o
f
in
f
o
r
m
atio
n
p
r
esen
t,
o
r
if
it
is
m
o
r
e
clo
s
ely
lin
k
e
d
to
lay
o
u
t,
s
tr
u
ctu
r
e,
o
r
in
d
iv
id
u
al
co
g
n
itiv
e
b
ias.
Fu
r
th
e
r
m
o
r
e,
th
e
p
o
s
itiv
e
ev
alu
atio
n
s
f
r
o
m
L
-
L
o
D
s
u
b
jects
s
u
g
g
est
a
d
is
co
n
n
ec
t
b
etwe
en
p
er
ce
p
tio
n
an
d
ac
tu
al
co
m
p
r
eh
e
n
s
io
n
p
er
f
o
r
m
an
ce
.
Sp
ec
if
ically
,
wh
ile
s
u
b
jects
v
iewe
d
th
e
m
o
d
el
f
av
o
u
r
a
b
ly
,
th
eir
ac
tu
al
u
n
d
er
s
tan
d
in
g
m
ay
h
av
e
b
ee
n
im
p
air
ed
b
y
th
e
lim
ited
in
f
o
r
m
atio
n
.
T
h
is
im
p
lies
a
f
o
r
m
o
f
illu
s
io
n
o
f
u
n
d
er
s
tan
d
i
n
g
,
wh
e
r
e
u
s
er
s
ar
e
u
n
awa
r
e
th
at
th
eir
p
er
ce
iv
e
d
c
lar
ity
an
d
ea
s
e
o
f
co
m
p
r
eh
en
s
i
o
n
ar
e
n
o
t r
ef
lectiv
e
o
f
th
ei
r
tr
u
e
co
m
p
r
eh
en
s
io
n
.
Fig
u
r
e
3
.
Su
b
jects’
p
er
ce
p
tio
n
o
f
th
e
UM
L
m
o
d
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
4
1
,
No
.
3
,
Ma
r
ch
20
2
6
:
1
0
9
5
-
1
1
0
4
1102
3
.
5
.
Dis
cus
s
io
n
I
n
th
is
s
tu
d
y
,
we
h
av
e
f
o
u
n
d
th
at
UM
L
d
iag
r
am
s
with
h
ig
h
L
o
D
s
ig
n
if
ican
tl
y
en
h
an
ce
co
m
p
r
eh
e
n
s
io
n
co
r
r
ec
tn
ess
.
Ho
wev
er
,
th
e
e
f
f
ec
t
o
n
co
m
p
r
eh
e
n
s
io
n
ef
f
icien
cy
w
as
n
o
t
s
tatis
tically
s
ig
n
if
ican
t.
I
n
th
e
o
r
ig
in
al
s
tu
d
y
,
h
ig
h
e
r
L
o
D
in
UM
L
d
iag
r
am
s
s
ig
n
if
ican
tly
im
p
r
o
v
ed
b
o
th
co
m
p
r
e
h
en
s
io
n
co
r
r
ec
tn
ess
an
d
e
f
f
icien
cy
.
W
e
id
en
tify
two
r
ea
s
o
n
s
th
at
m
ig
h
t
ex
p
lai
n
th
e
wea
k
e
r
ef
f
ec
t
o
n
co
m
p
r
eh
e
n
s
io
n
ef
f
icien
cy
.
First,
a
wea
k
e
r
ef
f
ec
t
o
n
co
m
p
r
eh
e
n
s
io
n
ef
f
icien
cy
m
ig
h
t
b
e
attr
ib
u
te
d
to
th
e
s
m
aller
s
am
p
le
s
ize
(
2
3
s
u
b
ject
s
)
co
m
p
a
r
ed
to
th
e
o
r
ig
in
al
(
5
3
s
u
b
ject
s
)
,
wh
ich
lik
ely
r
ed
u
ce
d
t
h
e
s
tatis
tical
p
o
wer
.
Seco
n
d
,
i
t
is
also
p
lau
s
ib
le
th
at
t
h
e
s
tu
d
e
n
ts
at
B
in
a
Nu
s
an
tar
a
Un
iv
er
s
ity
,
wh
o
h
ad
p
r
im
a
r
ily
th
e
o
r
etica
l
ex
p
o
s
u
r
e
to
UM
L
th
r
o
u
g
h
co
u
r
s
ewo
r
k
,
m
ay
n
o
t
h
av
e
b
e
n
ef
ited
f
r
o
m
t
h
e
h
i
g
h
e
r
L
o
D
to
th
e
s
am
e
e
x
ten
t
as
th
e
s
tu
d
en
ts
at
T
U/e
.
T
h
e
latter
r
eso
n
ate
s
with
th
e
f
in
d
in
g
s
o
f
C
r
u
z
-
L
em
u
s
et
al
.
[
1
3
]
,
wh
ich
s
u
g
g
est
th
at
in
c
r
ea
s
ed
d
etail
m
a
y
o
v
er
wh
elm
n
o
v
ice
u
s
er
s
,
p
ar
t
icu
lar
ly
in
s
m
aller
o
r
less
ex
p
er
ien
ce
d
s
am
p
les.
T
h
is
d
is
cr
ep
an
c
y
in
p
r
ac
tical
ex
p
er
ien
ce
m
a
y
h
a
v
e
m
o
d
er
at
ed
th
e
im
p
ac
t
o
f
L
o
D
o
n
c
o
m
p
r
eh
en
s
io
n
ef
f
icien
cy
.
Fro
m
a
r
esear
ch
p
er
s
p
ec
tiv
e,
t
h
e
r
esu
lt
o
f
th
is
s
tu
d
y
r
eso
n
ates
well
with
p
r
ev
io
u
s
wo
r
k
co
n
d
u
cted
b
y
th
e
r
esear
ch
er
[
6
]
-
[
8
]
,
[
1
0
]
.
F
u
r
th
er
,
t
h
is
r
ep
licatio
n
co
n
tr
i
b
u
tes
to
th
e
r
elativ
ely
lim
ited
p
o
o
l
o
f
em
p
ir
ical
r
ep
licatio
n
s
tu
d
ies
in
s
o
f
twar
e
en
g
in
ee
r
i
n
g
,
t
h
er
eb
y
s
tr
en
g
th
en
in
g
th
e
ev
id
e
n
ce
b
ase
r
e
g
ar
d
in
g
th
e
r
o
le
o
f
L
o
D
in
m
o
d
el
c
o
m
p
r
e
h
en
s
io
n
.
T
h
e
r
esu
lts
also
d
em
o
n
s
tr
ate
th
at
p
r
io
r
f
in
d
in
g
s
ar
e
t
r
an
s
f
er
ab
le
ac
r
o
s
s
d
if
f
er
en
t
c
o
n
tex
ts
-
th
at
is
,
o
u
r
s
tu
d
y
was
co
n
d
u
cted
with
a
d
is
tin
ct
s
u
b
ject
p
o
p
u
latio
n
(
I
n
d
o
n
esian
MSc
s
tu
d
en
ts
)
an
d
m
o
r
e
th
a
n
a
d
ec
ad
e
af
ter
th
e
o
r
ig
in
al
e
x
p
er
im
e
n
t.
Fro
m
a
p
r
ac
tical
s
tan
d
p
o
in
t,
o
n
e
im
p
o
r
tan
t
ta
k
ea
way
o
f
th
is
s
tu
d
y
is
th
at
s
o
f
twar
e
e
n
g
in
ee
r
in
g
ed
u
ca
to
r
s
an
d
p
r
ac
titi
o
n
e
r
s
s
h
o
u
ld
p
r
io
r
itize
in
clu
d
i
n
g
s
u
f
f
i
cien
t
d
etail
in
UM
L
d
iag
r
am
s
,
p
ar
ticu
lar
ly
wh
e
n
th
ey
ar
e
i
n
ten
d
ed
f
o
r
co
m
m
u
n
icatio
n
o
r
in
s
tr
u
ctio
n
al
p
u
r
p
o
s
es.
Alth
o
u
g
h
s
im
p
lifie
d
m
o
d
els
m
ay
r
ed
u
ce
v
is
u
al
co
m
p
lex
ity
,
t
h
ey
r
is
k
o
m
itti
n
g
c
r
itical
s
em
an
tic
cu
es
th
at
aid
co
m
p
r
eh
en
s
io
n
,
esp
ec
ially
f
o
r
n
o
v
ice
o
r
less
ex
p
er
ien
ce
d
s
tak
eh
o
l
d
er
s
.
I
t
is
also
im
p
o
r
tan
t
t
o
n
o
te
th
at
UM
L
m
o
d
els
with
lo
w
L
o
D
m
ig
h
t
ca
u
s
e
an
illu
s
io
n
o
f
u
n
d
er
s
tan
d
in
g
,
wh
e
r
e
u
s
er
s
ar
e
u
n
awa
r
e
th
at
th
eir
p
er
ce
iv
ed
clar
ity
a
n
d
ea
s
e
o
f
co
m
p
r
eh
e
n
s
io
n
ar
e
n
o
t r
ef
lectiv
e
o
f
th
eir
t
r
u
e
co
m
p
r
eh
en
s
io
n
.
T
h
e
r
e
f
o
r
e
,
w
e
s
u
g
g
e
s
t
t
h
r
e
e
p
r
a
c
t
i
c
al
r
e
c
o
m
m
e
n
d
a
t
i
o
n
s
f
o
r
u
s
i
n
g
U
M
L
d
i
a
g
r
a
m
s
.
F
i
r
s
t
,
u
s
e
m
u
l
ti
p
l
e
U
M
L
d
i
a
g
r
a
m
t
y
p
e
s
t
o
r
e
p
r
e
s
e
n
t
m
u
l
ti
p
l
e
v
i
e
w
p
o
i
n
t
s
,
n
a
m
e
l
y
u
s
e
c
a
s
e
d
i
a
g
r
a
m
(
s
y
s
t
e
m
r
e
q
u
i
r
e
m
e
n
ts
)
,
s
e
q
u
e
n
c
e
d
i
a
g
r
a
m
(
c
o
m
p
o
n
e
n
t
i
n
t
e
r
a
c
t
i
o
n
s
)
,
a
n
d
c
l
a
s
s
d
i
ag
r
a
m
s
(
s
t
r
u
c
t
u
r
a
l
r
e
l
at
i
o
n
s
h
i
p
s
)
.
S
e
c
o
n
d
,
s
p
e
c
i
f
y
i
m
p
o
r
t
a
n
t
a
t
t
r
i
b
u
t
e
s
o
r
c
o
n
c
e
p
ts
a
c
r
o
s
s
UM
L
d
i
a
g
r
a
m
s
.
F
o
r
ex
a
m
p
l
e
,
a
l
l
cl
a
s
s
es
i
n
a
cl
a
s
s
d
ia
g
r
a
m
m
u
s
t
s
p
ec
i
f
y
c
l
a
s
s
a
t
t
r
i
b
u
te
s
a
n
d
m
e
t
h
o
d
s
e
s
s
e
n
ti
a
l
f
o
r
u
n
d
e
r
s
t
a
n
d
i
n
g
t
h
e
s
y
s
t
e
m
.
Si
m
i
la
r
l
y
,
a
l
l
m
es
s
a
g
es
i
n
s
e
q
u
e
n
c
e
d
i
a
g
r
a
m
s
m
u
s
t
a
ls
o
s
p
e
c
i
f
y
c
r
i
ti
c
a
l
m
e
s
s
a
g
e
p
a
r
a
m
e
t
e
r
s
.
F
i
n
a
ll
y
,
m
a
i
n
t
a
i
n
c
o
n
s
i
s
t
e
n
c
y
a
c
r
o
s
s
a
l
l
U
M
L
d
i
a
g
r
a
m
s
–
f
o
r
e
x
a
m
p
l
e
,
m
ess
a
g
e
s
a
p
p
e
a
r
i
n
g
i
n
a
s
e
q
u
e
n
c
e
d
i
a
g
r
a
m
m
u
s
t
a
ls
o
b
e
c
o
n
s
is
t
e
n
tl
y
s
p
e
c
i
f
i
e
d
i
n
t
h
e
c
l
ass
d
i
a
g
r
a
m
.
I
t
i
s
a
l
s
o
i
m
p
o
r
t
a
n
t
to
n
o
t
e
t
h
a
t
U
M
L
m
o
d
e
l
i
n
g
t
o
o
l
s
c
o
u
l
d
b
e
e
n
h
a
n
c
e
d
t
o
s
u
p
p
o
r
t
f
l
e
x
i
b
l
e
v
i
e
w
s
,
a
l
l
o
w
i
n
g
u
s
e
r
s
t
o
t
o
g
g
l
e
b
e
t
w
ee
n
l
o
w
a
n
d
h
i
g
h
L
o
D
p
r
e
s
e
n
t
ati
o
n
s
d
e
p
e
n
d
i
n
g
o
n
t
h
e
t
a
s
k
o
r
au
d
i
e
n
c
e
n
e
e
d
s
.
C
o
n
s
id
er
in
g
th
e
af
o
r
em
en
tio
n
ed
p
o
in
ts
,
f
u
r
th
er
wo
r
k
is
s
till
n
ee
d
ed
t
o
in
v
esti
g
ate
L
o
D
in
UM
L
m
o
d
els.
I
n
p
ar
ticu
la
r
,
we
u
n
d
er
lin
e
th
e
im
p
o
r
tan
ce
o
f
co
n
d
u
ctin
g
m
o
r
e
e
x
p
er
im
e
n
tal
r
ep
l
icatio
n
s
to
v
alid
ate
th
e
r
esu
lts
o
f
th
is
s
tu
d
y
.
Fu
r
th
er
m
o
r
e,
with
th
e
a
d
v
a
n
ce
m
en
t
o
f
to
o
lin
g
th
at
aid
s
s
o
f
twar
e
en
g
in
ee
r
s
in
cr
ea
tin
g
a
n
d
m
ain
tain
in
g
U
ML
d
iag
r
a
m
s
,
in
clu
d
in
g
th
o
s
e
p
o
wer
e
d
b
y
Gen
er
ativ
e
AI
,
it
is
im
p
o
r
tan
t
to
in
v
esti
g
ate
th
e
im
p
ac
t o
f
L
o
D
in
s
u
ch
co
n
te
x
ts
.
3
.
6
.
T
hrea
t
s
t
o
v
a
lid
it
y
W
e
r
ec
o
g
n
ize
s
ev
er
al
p
o
ten
t
ia
l
th
r
ea
ts
to
th
e
v
alid
ity
o
f
o
u
r
f
in
d
in
g
s
.
First,
i
n
ter
n
al
v
alid
ity
m
ay
b
e
af
f
ec
ted
b
y
u
n
c
o
n
tr
o
lled
f
ac
to
r
s
,
s
u
ch
as
in
d
iv
id
u
al
d
if
f
er
en
ce
s
in
p
r
io
r
UM
L
k
n
o
wled
g
e,
r
ea
s
o
n
in
g
ab
ilit
y
,
o
r
f
am
iliar
ity
with
th
e
p
r
o
b
le
m
d
o
m
ai
n
.
W
h
ile
we
en
s
u
r
ed
th
at
all
s
u
b
je
ct
s
h
ad
r
ec
eiv
e
d
co
m
p
ar
ab
le
UM
L
tr
ain
in
g
,
we
d
id
n
o
t
ass
ess
t
h
eir
p
r
io
r
e
x
p
er
ien
ce
in
d
ep
t
h
.
Seco
n
d
,
th
e
f
ac
t
th
at
t
h
e
s
u
b
ject
s
wer
e
MSc
s
tu
d
en
ts
m
ay
lim
it
e
x
ter
n
al
v
alid
ity
,
i.e
.
,
r
ed
u
cin
g
g
en
e
r
aliza
b
ilit
y
to
in
d
u
s
tr
ial
p
r
ac
t
itio
n
er
s
.
T
h
ir
d
,
t
h
e
m
ea
s
u
r
es
f
o
r
co
m
p
r
eh
en
s
io
n
co
r
r
ec
tn
ess
an
d
ef
f
icien
cy
r
ely
o
n
a
15
-
q
u
esti
o
n
q
u
esti
o
n
n
air
e.
W
h
ile
th
e
q
u
esti
o
n
s
wer
e
ca
r
ef
u
lly
d
esi
g
n
ed
to
r
e
f
lect
u
n
d
er
s
tan
d
i
n
g
o
f
th
e
UM
L
m
o
d
el
,
th
e
y
m
ay
n
o
t
ca
p
tu
r
e
th
e
f
u
ll
co
m
p
lex
ity
o
f
m
o
d
el
co
m
p
r
e
h
en
s
io
n
in
r
ea
l
-
wo
r
ld
s
ettin
g
s
,
wh
ich
m
a
y
af
f
ec
t
c
o
n
s
tr
u
c
t
v
alid
ity
.
Fin
ally
,
s
tatis
t
ical
p
o
wer
m
ay
b
e
lim
ited
d
u
e
to
th
e
r
elativ
ely
s
m
al
l
s
am
p
le
s
ize
,
wh
ich
m
ay
af
f
ec
t
th
e
c
o
n
clu
s
io
n
v
alid
ity
.
Desp
ite
th
ese
lim
itatio
n
s
,
o
u
r
s
tu
d
y
p
r
o
v
id
es
v
alu
a
b
le
in
s
ig
h
ts
in
t
o
th
e
r
o
le
o
f
L
o
D
in
UM
L
m
o
d
el
co
m
p
r
eh
e
n
s
io
n
an
d
o
f
f
er
s
a
s
o
lid
f
o
u
n
d
atio
n
f
o
r
f
u
r
th
e
r
em
p
i
r
ical
wo
r
k
in
t
h
is
ar
ea
.
4.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
r
ep
licate
s
an
ea
r
lie
r
ex
p
er
im
en
t
in
v
esti
g
atin
g
th
e
r
o
le
o
f
L
o
D
in
UM
L
m
o
d
els
o
n
m
o
d
el
co
m
p
r
eh
e
n
s
io
n
.
W
h
ile
th
e
o
r
ig
in
al
s
tu
d
y
co
n
cl
u
d
ed
th
at
h
ig
h
e
r
L
o
D
s
ig
n
if
ican
t
ly
im
p
r
o
v
es
b
o
th
co
m
p
r
eh
e
n
s
io
n
co
r
r
ec
t
n
ess
an
d
ef
f
icien
cy
,
it
also
ca
lled
f
o
r
f
u
r
th
er
r
e
p
licatio
n
.
Ou
r
r
ep
lic
atio
n
co
n
f
ir
m
s
th
at
h
ig
h
er
L
o
D
im
p
r
o
v
es
co
m
p
r
eh
en
s
io
n
co
r
r
ec
tn
ess
am
o
n
g
2
3
MSc
C
o
m
p
u
ter
Scien
ce
s
tu
d
en
ts
at
B
in
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Leve
l o
f d
eta
il in
UML mo
d
els a
n
d
its
imp
a
ct
o
n
mo
d
el
c
o
mp
r
eh
en
s
io
n
…
(
A
r
ia
d
i Nu
g
r
o
h
o
)
1103
Nu
s
an
tar
a
Un
iv
er
s
ity
.
Ho
wev
er
,
th
e
ef
f
ec
t
o
n
c
o
m
p
r
e
h
e
n
s
io
n
ef
f
icien
cy
was
n
o
t
s
tatis
tically
s
ig
n
if
ican
t,
lik
ely
d
u
e
t
o
th
e
s
m
aller
s
am
p
le
s
ize
an
d
co
n
tex
tu
al
f
ac
t
o
r
s
s
u
ch
as
s
tu
d
en
ts
’
th
eo
r
etic
al
b
ac
k
g
r
o
u
n
d
an
d
lim
ited
p
r
ac
tical
m
o
d
elin
g
ex
p
er
ien
ce
.
T
h
ese
f
in
d
in
g
s
r
ein
f
o
r
ce
th
e
r
elev
an
ce
o
f
L
o
D
an
d
h
ig
h
lig
h
t
th
e
n
ee
d
f
o
r
ca
r
ef
u
lly
b
alan
ci
n
g
d
etail
an
d
clar
ity
in
UM
L
d
iag
r
am
s
.
B
ased
o
n
th
ese
r
esu
lts
,
we
a
ls
o
h
ig
h
lig
h
t
th
r
ee
p
r
ac
tical
r
ec
o
m
m
e
n
d
atio
n
s
f
o
r
u
s
in
g
UM
L
d
ia
g
r
am
s
.
First,
u
s
e
m
u
ltip
le
UM
L
d
iag
r
am
ty
p
es
to
r
ep
r
esen
t
d
if
f
er
en
t
v
iewp
o
i
n
ts
.
Seco
n
d
,
s
p
ec
if
y
im
p
o
r
tan
t
attr
i
b
u
te
s
o
r
c
o
n
ce
p
ts
ac
r
o
s
s
UM
L
d
iag
r
am
s
.
Fin
ally
,
m
ain
tain
co
n
s
is
ten
cy
ac
r
o
s
s
al
l U
ML
d
iag
r
am
s
.
Fu
tu
r
e
r
esear
ch
s
h
o
u
ld
c
o
n
s
id
er
:
(
1
)
co
n
d
u
ctin
g
r
ep
licatio
n
s
tu
d
ies
with
lar
g
er
an
d
m
o
r
e
d
iv
er
s
e
s
am
p
les,
in
clu
d
in
g
p
r
o
f
ess
io
n
al
s
o
f
twar
e
en
g
i
n
ee
r
s
,
(
2
)
i
n
v
e
s
tig
atin
g
th
e
im
p
ac
t
o
f
ad
v
a
n
c
ed
to
o
lin
g
th
at
ca
n
b
e
u
s
ed
to
ass
is
t
s
o
f
tw
ar
e
en
g
in
ee
r
s
in
m
ain
tain
in
g
UM
L
m
o
d
els
o
f
v
a
r
y
in
g
lev
els
o
f
d
et
ail,
in
clu
d
in
g
t
h
o
s
e
au
g
m
en
ted
with
Gen
er
ativ
e
AI
.
Su
ch
ef
f
o
r
ts
wo
u
ld
c
o
n
tr
ib
u
te
to
a
d
ee
p
er
u
n
d
er
s
tan
d
in
g
o
f
h
o
w
v
is
u
al
d
etail
s
u
p
p
o
r
ts
m
o
d
el
co
m
p
r
eh
en
s
io
n
an
d
i
n
f
o
r
m
b
etter
m
o
d
elin
g
p
r
ac
tice
s
in
b
o
th
ac
a
d
em
ia
an
d
in
d
u
s
tr
y
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
e
au
th
o
r
s
s
tate
th
at
th
er
e
was n
o
f
u
n
d
in
g
f
o
r
t
h
e
d
ev
el
o
p
m
en
t o
f
th
e
r
esear
ch
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
t
r
ib
u
to
r
R
o
les
T
a
x
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
i
d
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Ar
iad
i N
u
g
r
o
h
o
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Mic
h
el
R
.
V
C
h
au
d
r
o
n
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
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o
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Fo
:
Fo
r
mal
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n
a
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y
s
i
s
I
:
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n
v
e
s
t
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g
a
t
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n
R
:
R
e
so
u
r
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e
s
D
:
D
a
t
a
C
u
r
a
t
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o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
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r
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g
-
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t
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su
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p
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t
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Fu
:
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n
d
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a
c
q
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t
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CO
NF
L
I
C
T
O
F
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N
T
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R
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S
T
ST
A
T
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M
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NT
T
h
e
au
th
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r
s
d
ec
lar
e
th
at
th
er
e
ar
e
n
o
f
in
an
cial,
p
er
s
o
n
al,
o
r
p
r
o
f
ess
io
n
al
co
n
f
licts
o
f
in
ter
est
th
a
t
co
u
ld
h
a
v
e
in
f
lu
e
n
ce
d
th
e
r
esu
lts
o
r
in
ter
p
r
etatio
n
s
o
f
th
is
r
esear
ch
.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
th
at
s
u
p
p
o
r
t
t
h
e
f
in
d
in
g
s
o
f
th
is
s
tu
d
y
a
r
e
av
ailab
l
e
f
r
o
m
th
e
co
r
r
esp
o
n
d
in
g
a
u
th
o
r
,
Ar
iad
i
Nu
g
r
o
h
o
,
u
p
o
n
r
ea
s
o
n
ab
le
r
eq
u
est.
RE
F
E
R
E
NC
E
S
[
1
]
H
.
C
.
P
u
r
c
h
a
se,
L.
C
o
l
p
o
y
s,
M
.
M
c
G
i
l
l
,
D
.
C
a
r
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d
i
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s
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larly
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rl
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is
th
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fo
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n
d
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o
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n
su
lt
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(
k
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d
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c
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lt
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.
c
o
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),
a
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k
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rta
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b
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se
d
a
d
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firm
th
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t
h
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lp
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iza
ti
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s
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li
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tec
h
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lo
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teg
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with
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u
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ss
g
o
a
ls.
P
ri
o
r
t
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h
e
h
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ld
se
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ro
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b
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y
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e
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v
iro
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m
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ts.
Aria
d
i
is
a
m
e
m
b
e
r
o
f
IEE
E
a
n
d
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C
o
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p
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ter
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o
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.
He
c
a
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e
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a
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d
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t
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riad
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n
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g
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h
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@b
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n
u
s.a
c
.
id
.
Mi
c
h
e
l
R.V
Ch
a
u
d
r
o
n
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F
u
l
l
P
ro
fe
ss
o
r
a
n
d
Ch
a
ir
o
f
th
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S
o
f
t
wa
re
En
g
i
n
e
e
rin
g
g
ro
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p
a
t
t
h
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TU
Ei
n
d
h
o
v
e
n
wh
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c
h
is
p
a
rt
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f
th
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De
p
a
rtme
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t
o
f
M
a
th
e
m
a
ti
c
s
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
.
P
ri
o
r
to
th
is,
h
e
wo
rk
e
d
a
t
Un
i
v
e
rsiti
e
s
i
n
G
o
th
e
n
b
u
r
g
(Ch
a
lme
rs|G
U),
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e
n
a
n
d
Ei
n
d
h
o
v
e
n
i
n
th
e
Ne
th
e
rlan
d
s.
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o
b
tain
e
d
h
is
P
h
.
D.
i
n
th
e
a
r
e
a
o
f
fo
rm
a
l
m
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th
o
d
s
a
n
d
p
ro
g
ra
m
m
in
g
c
a
lcu
li
f
o
r
p
a
ra
ll
e
l
c
o
m
p
u
ti
n
g
.
His res
e
a
rc
h
in
tere
sts a
re
in
so
ftwa
re
a
rc
h
it
e
c
tu
re
,
so
ftwa
re
d
e
sig
n
,
so
ftwa
re
m
o
d
e
li
n
g
with
a
sp
e
c
ial
fo
c
u
s
o
n
UM
L
,
so
ftwa
re
c
o
m
p
o
siti
o
n
a
n
d
k
n
o
wle
d
g
e
s
h
a
ri
n
g
.
Re
c
e
n
tl
y
,
u
se
o
f
AI (Arti
ficia
l
In
telli
g
e
n
c
e
)
fo
r
S
o
ftwa
re
De
v
e
lo
p
m
e
n
t.
He
h
a
s
a
n
i
n
tere
st
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e
m
p
iri
c
a
l
stu
d
i
e
s
in
so
ftwa
re
e
n
g
in
e
e
rin
g
e
sp
e
c
ially
i
n
t
h
e
a
fo
re
m
e
n
ti
o
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e
d
a
re
a
s
a
n
d
p
re
fe
ra
b
ly
i
n
in
d
u
stri
a
l
c
o
n
tex
ts.
He
su
p
p
o
rts
se
v
e
ra
l
c
o
n
fe
re
n
c
e
s
a
n
d
jo
u
r
n
a
ls
in
c
lu
d
in
g
(C
o
n
f:)
ICS
E
,
M
OD
EL
S
a
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d
E
u
ro
m
icr
o
S
EAA,
F
AM
ECS
E
a
n
d
(J
n
l:
)
S
o
S
y
M
a
n
d
Emp
iri
c
a
l
S
t
u
d
ies
i
n
S
o
ftwa
re
En
g
in
e
e
rin
g
(E
M
S
E).
He
h
a
s
g
iv
e
n
tu
to
rials
a
n
d
g
u
e
st
lec
tu
re
s
o
n
S
o
f
twa
re
Arc
h
it
e
c
tu
re
De
sig
n
a
n
d
M
o
d
e
li
n
g
a
s
we
ll
a
s
o
n
Emp
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rica
l
S
o
ft
wa
re
E
n
g
i
n
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rin
g
Re
se
a
rc
h
M
e
th
o
d
s a
s g
u
e
st l
e
c
tu
r
e
r
in
(o
.
a
.
)
S
p
a
i
n
,
T
u
n
e
sia
,
F
ra
n
c
e
,
F
in
lan
d
,
S
l
o
v
a
k
ia a
n
d
T
h
e
Ne
th
e
rlan
d
s.
He
c
a
n
b
e
re
a
c
h
e
d
a
t
m
.
r.
v
.
c
h
a
u
d
r
o
n
@t
u
e
.
n
l
.
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