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1.
I
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D
UCT
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n
s
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in
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th
e
q
u
ality
o
f
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s
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war
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s
y
s
tem
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SS
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d
e
p
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d
s
h
ea
v
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o
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r
i
g
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s
test
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g
.
I
n
d
u
s
tr
y
ex
p
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ts
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ate
th
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ca
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s
u
m
e
4
0
-
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%
o
f
a
co
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p
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s
o
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twar
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ce
s
,
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ef
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im
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tan
ce
in
m
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g
r
eliab
ilit
y
,
s
ec
u
r
ity
,
a
n
d
ef
f
i
cien
cy
[
1
]
.
W
ith
o
u
t
a
s
tr
u
ctu
r
ed
test
in
g
p
r
o
c
ess
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s
o
f
twar
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m
e
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lead
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s
s
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ity
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a
n
d
r
ed
u
ce
d
s
y
s
tem
p
er
f
o
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m
an
ce
[
2
]
.
So
f
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test
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ty
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ically
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ateg
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ized
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n
to
two
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r
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a
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d
b
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,
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ed
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t
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g
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k
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aly
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I
t
en
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r
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th
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co
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al
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s
s
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n
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lack
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test
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o
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s
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o
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th
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y
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tem
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s
in
p
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-
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t
p
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t
b
e
h
av
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r
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o
u
t
ex
am
in
in
g
in
ter
n
al
co
d
e
ex
e
cu
tio
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[
3
]
.
T
h
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is
p
ar
ticu
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ly
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s
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l
f
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im
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y
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te
m
m
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ts
its
in
ten
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r
eq
u
ir
em
en
ts
[
4
]
.
T
h
e
ev
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l
u
tio
n
o
f
m
o
d
er
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s
o
f
twar
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h
a
s
in
tr
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d
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n
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m
p
lex
ities
,
p
ar
ticu
lar
l
y
with
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ig
d
ata
in
teg
r
atio
n
.
As
s
o
f
twar
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s
y
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tem
s
b
ec
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m
e
m
o
r
e
r
elian
t
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m
ass
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d
atasets
,
tr
ad
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al
test
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d
s
s
tr
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g
le
to
k
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B
ig
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ata
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n
o
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ju
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t
ab
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lu
m
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-
it
also
in
v
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lv
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d
if
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es),
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city
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r
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ata
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s
in
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)
,
an
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ac
ity
(
d
ata
ac
cu
r
ac
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d
c
o
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s
is
ten
cy
)
[
5
]
.
T
h
ese
f
ac
to
r
s
co
m
p
licate
s
o
f
twar
e
v
alid
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n
,
m
ak
in
g
it
ch
allen
g
in
g
to
en
s
u
r
e
th
at
test
ca
s
es
s
u
f
f
icien
tly
co
v
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r
all
p
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ten
tial
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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I
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N:
2252
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8
7
7
6
E
xp
lo
r
in
g
d
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p
ers
p
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tives:
en
h
a
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b
la
ck
b
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x
test
in
g
th
r
o
u
g
h
m
a
ch
in
e
…
(
Heb
a
N
a
fez Ja
la
l
)
239
s
y
s
tem
b
eh
av
io
r
s
.
Scalab
ilit
y
co
n
ce
r
n
s
ar
e
a
m
ajo
r
lim
itatio
n
o
f
co
n
v
e
n
tio
n
al
test
in
g
m
eth
o
d
o
l
o
g
ies.
T
r
ad
itio
n
al
eq
u
i
v
alen
ce
p
ar
tit
io
n
in
g
an
d
b
o
u
n
d
a
r
y
an
aly
s
is
tech
n
iq
u
es,
wh
ile
ef
f
ec
tiv
e
f
o
r
s
m
all
d
atasets
,
o
f
ten
g
en
er
ate
a
n
ex
ce
s
s
iv
e
n
u
m
b
er
o
f
te
s
t
ca
s
es
wh
en
ap
p
lied
to
b
i
g
d
ata
en
v
ir
o
n
m
en
ts
[
6
]
.
T
h
is
lead
s
to
p
r
o
lo
n
g
ed
test
in
g
cy
cles a
n
d
in
ef
f
icien
t r
eso
u
r
ce
u
tili
za
tio
n
,
r
eq
u
ir
in
g
n
ew,
in
tellig
en
t a
p
p
r
o
ac
h
es to
o
p
tim
ize
test
ca
s
e
s
elec
tio
n
[
7
]
.
Giv
en
th
ese
c
h
allen
g
es,
a
r
tif
icial
in
tellig
en
ce
(
AI
)
a
n
d
m
ac
h
in
e
lear
n
in
g
(
ML
)
o
f
f
er
p
r
o
m
is
in
g
s
o
lu
tio
n
s
f
o
r
en
h
a
n
cin
g
s
o
f
twar
e
test
in
g
.
B
y
lev
e
r
ag
in
g
AI
-
d
r
iv
en
ap
p
r
o
ac
h
es,
test
ca
s
es
ca
n
b
e
p
r
i
o
r
itized
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tellig
en
tly
,
r
e
d
u
cin
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r
e
d
u
n
d
an
cy
a
n
d
o
p
tim
izin
g
r
eso
u
r
ce
allo
ca
tio
n
.
T
h
is
s
tu
d
y
in
tr
o
d
u
ce
s
a
h
y
b
r
id
ML
m
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d
el
th
at
in
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r
ates
d
ec
is
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tr
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s
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d
g
e
n
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alg
o
r
ith
m
s
(
GA)
to
r
ef
in
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test
ca
s
e
s
elec
tio
n
.
Dec
is
io
n
tr
ee
s
ex
ce
l
in
id
en
tify
in
g
p
atter
n
s
with
in
test
d
ata,
o
f
f
er
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a
s
tr
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c
tu
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ap
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to
class
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t
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ased
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s
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s
s
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y
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en
s
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r
in
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th
at
o
n
ly
th
e
m
o
s
t
im
p
ac
tf
u
l
test
ca
s
es
ar
e
ex
ec
u
ted
.
T
o
g
eth
er
,
th
ese
tech
n
iq
u
es
p
r
o
v
id
e
a
b
alan
ce
d
ap
p
r
o
ac
h
-
d
e
c
i
s
i
o
n
t
r
ee
s
e
n
a
b
l
e
s
t
r
u
c
t
u
r
e
d
p
r
i
o
r
i
t
i
z
a
ti
o
n
,
w
h
i
l
e
GA
e
n
h
a
n
c
e
a
d
a
p
t
a
b
i
li
t
y
i
n
d
y
n
a
m
i
c
t
es
t
e
n
v
i
r
o
n
m
e
n
t
s
[
8
]
.
T
h
is
r
esear
ch
a
d
d
r
ess
es
k
ey
ch
allen
g
es
i
n
b
lac
k
b
o
x
test
in
g
b
y
an
aly
zin
g
th
e
lim
itatio
n
s
o
f
tr
ad
itio
n
al
test
s
elec
t
io
n
tech
n
iq
u
es in
b
ig
d
ata
en
v
ir
o
n
m
en
ts
[
9
]
,
d
ev
el
o
p
in
g
a
ML
-
b
ased
p
r
io
r
itizatio
n
m
o
d
el
to
o
p
tim
ize
test
ca
s
e
s
elec
ti
o
n
,
an
d
v
alid
atin
g
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
th
r
o
u
g
h
p
er
f
o
r
m
an
ce
ev
alu
atio
n
,
d
em
o
n
s
tr
atin
g
its
im
p
ac
t
o
n
test
in
g
ef
f
icien
cy
a
n
d
ac
c
u
r
a
cy
[
1
0
]
.
E
x
p
er
im
e
n
tal
r
esu
lts
in
d
icate
th
at
t
h
is
ap
p
r
o
ac
h
ac
h
iev
es
a
9
6
%
ac
cu
r
ac
y
r
ate
an
d
a
9
0
%
s
u
cc
e
s
s
r
ate
in
id
en
tify
in
g
r
elev
an
t
test
ca
s
es.
T
h
es
e
f
in
d
in
g
s
h
ig
h
lig
h
t
th
e
p
o
te
n
tial
o
f
AI
-
d
r
iv
e
n
tech
n
iq
u
es
in
im
p
r
o
v
in
g
test
in
g
ef
f
icien
cy
,
r
ed
u
cin
g
c
o
s
ts
,
an
d
ad
ap
tin
g
to
lar
g
e
-
s
ca
le
s
o
f
tw
ar
e
en
v
ir
o
n
m
en
ts
.
B
y
in
te
g
r
a
tin
g
in
tellig
en
t
test
s
elec
tio
n
m
ec
h
an
is
m
s
,
th
is
s
tu
d
y
p
r
o
v
id
es
a
s
ca
lab
le
an
d
ad
ap
tab
le
s
o
lu
tio
n
f
o
r
m
o
d
e
r
n
s
o
f
twar
e
test
in
g
c
h
allen
g
es,
p
av
in
g
th
e
way
f
o
r
m
o
r
e
ef
f
icien
t,
au
to
m
ate
d
,
an
d
r
eso
u
r
ce
-
o
p
tim
ized
test
in
g
f
r
a
m
ewo
r
k
s
.
2.
B
ACK
G
RO
UND
AN
D
RE
L
AT
E
D
WO
RK
So
f
twar
e
test
in
g
co
n
tin
u
es
to
ev
o
lv
e
as
m
o
d
er
n
a
p
p
licatio
n
s
g
r
o
w
in
c
o
m
p
lex
ity
.
T
h
e
in
cr
ea
s
ed
r
elian
ce
o
n
b
ig
d
ata
h
as
i
n
tr
o
d
u
ce
d
n
ew
ch
allen
g
es
in
v
ali
d
atio
n
p
r
o
ce
s
s
es,
r
eq
u
ir
in
g
ef
f
icien
t
m
eth
o
d
s
to
en
s
u
r
e
s
o
f
twar
e
r
eliab
ilit
y
.
T
r
ad
itio
n
al
ap
p
r
o
ac
h
es,
wh
ile
e
f
f
ec
tiv
e
in
co
n
tr
o
lled
e
n
v
ir
o
n
m
en
ts
,
o
f
ten
f
ail
t
o
s
ca
le
wh
en
d
ea
lin
g
with
lar
g
e
an
d
d
y
n
am
ically
ch
an
g
in
g
d
a
tasets
.
As
a
r
esu
lt,
r
esear
ch
ef
f
o
r
ts
h
av
e
f
o
cu
s
ed
o
n
lev
er
a
g
in
g
ML
tec
h
n
iq
u
es t
o
im
p
r
o
v
e
test
ex
ec
u
tio
n
an
d
c
o
v
er
ag
e.
2
.
1
.
B
a
c
k
g
ro
un
d
B
ig
d
ata
an
aly
tics
s
ig
n
if
ica
n
tly
en
h
an
ce
s
s
o
f
twar
e
test
in
g
b
y
im
p
r
o
v
in
g
test
ca
s
e
s
elec
tio
n
,
d
ef
ec
t
p
r
ed
ictio
n
,
an
d
au
to
m
atio
n
.
Fig
u
r
e
1
illu
s
tr
ates
h
o
w
b
ig
d
at
a
attr
ib
u
tes
in
f
lu
en
ce
s
o
f
twar
e
v
alid
atio
n
.
Un
lik
e
tr
ad
itio
n
al
ap
p
r
o
ac
h
es,
AI
-
p
o
wer
ed
m
o
d
els
d
y
n
am
ically
p
r
io
r
itize
test
ca
s
es
b
ased
o
n
r
elev
an
ce
,
m
in
im
izin
g
ex
ec
u
tio
n
o
v
er
h
ea
d
[
1
1
]
.
P
r
e
v
i
o
u
s
s
t
u
d
i
es
h
i
g
h
li
g
h
t
t
h
e
ef
f
e
c
t
i
v
e
n
e
s
s
o
f
d
e
ci
s
i
o
n
t
r
e
es
an
d
GA
i
n
o
p
ti
m
i
zi
n
g
t
es
t
i
n
g
f
r
a
m
e
w
o
r
k
s
.
D
e
c
is
i
o
n
t
r
e
es
s
t
r
u
ct
u
r
e
c
l
ass
i
f
i
c
at
i
o
n
,
s
e
g
m
e
n
t
i
n
g
t
e
s
t
ca
s
es
b
a
s
e
d
o
n
f
e
a
t
u
r
e
s
i
g
n
i
f
i
c
a
n
c
e
,
w
h
i
le
GA
c
o
n
t
i
n
u
o
u
s
l
y
r
e
f
i
n
e
p
r
i
o
r
i
t
i
za
t
io
n
f
o
r
b
e
t
t
e
r
d
e
f
e
ct
d
e
t
ec
t
i
o
n
[
1
2
]
.
T
h
i
s
c
o
m
b
i
n
at
i
o
n
r
e
d
u
c
es
c
o
m
p
u
t
a
t
i
o
n
a
l
c
o
s
ts
w
h
i
l
e
m
a
i
n
ta
i
n
i
n
g
h
i
g
h
t
e
s
t
c
o
v
e
r
a
g
e
.
C
o
n
v
e
n
t
i
o
n
a
l
t
es
t
i
n
g
m
e
t
h
o
d
s
l
i
k
e
e
q
u
i
v
a
l
e
n
ce
p
a
r
t
it
i
o
n
i
n
g
a
n
d
b
o
u
n
d
a
r
y
a
n
a
l
y
s
is
s
t
r
u
g
g
l
e
w
it
h
l
a
r
g
e
d
a
t
a
s
et
s
,
l
e
a
d
i
n
g
t
o
r
e
s
o
u
r
c
e
-
i
n
t
e
n
s
i
v
e
p
r
o
c
e
s
s
es
.
M
L
-
d
r
i
v
e
n
t
e
c
h
n
i
q
u
e
s
p
r
o
v
i
d
e
a
d
a
p
t
i
v
e
s
o
l
u
t
i
o
n
s
,
f
il
t
e
r
i
n
g
r
e
d
u
n
d
a
n
t
t
e
s
t
ca
s
es
a
n
d
e
n
h
a
n
c
i
n
g
d
e
f
e
c
t
i
d
e
n
t
i
f
ic
a
t
i
o
n
[
1
3
]
.
Fig
u
r
e
1
.
B
ig
d
ata
c
h
ar
ac
ter
is
tics
an
d
ap
p
licatio
n
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
1
,
Ma
r
ch
20
26
:
23
8
-
24
6
240
A
u
t
o
m
a
t
e
d
t
es
t
c
as
e
g
e
n
e
r
a
t
i
o
n
f
u
r
t
h
e
r
r
e
d
u
c
e
s
r
e
d
u
n
d
a
n
c
y
w
h
i
l
e
m
a
i
n
ta
i
n
i
n
g
c
o
v
e
r
a
g
e
.
C
o
m
p
a
r
a
t
i
v
e
s
t
u
d
i
es
i
n
d
i
c
at
e
t
h
a
t
A
I
-
b
a
s
ed
m
o
d
e
l
s
o
u
t
p
e
r
f
o
r
m
t
r
a
d
i
t
i
o
n
a
l
s
t
r
at
e
g
i
es
i
n
o
p
t
i
m
i
z
i
n
g
te
s
t
e
x
e
c
u
t
i
o
n
[
1
4
]
.
R
es
e
a
r
c
h
i
n
A
I
-
d
r
i
v
e
n
s
o
f
t
wa
r
e
t
e
s
ti
n
g
h
a
s
e
x
p
l
o
r
e
d
ML
t
e
ch
n
i
q
u
e
s
s
u
c
h
a
s
s
u
p
p
o
r
t
v
e
c
t
o
r
m
a
c
h
i
n
e
s
(
S
VM
s
)
,
n
e
u
r
a
l
n
e
t
w
o
r
k
s
,
a
n
d
r
e
i
n
f
o
r
c
em
e
n
t
l
e
a
r
n
i
n
g
t
o
a
u
t
o
m
a
te
t
es
t
s
e
l
e
ct
i
o
n
a
n
d
i
m
p
r
o
v
e
e
f
f
i
c
i
e
n
c
y.
A
k
ey
ch
allen
g
e
in
AI
-
d
r
iv
e
n
test
in
g
is
its
r
elian
ce
o
n
lar
g
e,
lab
eled
d
atasets
f
o
r
ac
cu
r
ac
y
.
So
m
e
s
tu
d
ies
p
r
o
p
o
s
e
s
em
i
-
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
lear
n
in
g
as
alter
n
ativ
es,
im
p
r
o
v
in
g
ap
p
lic
ab
ilit
y
in
r
ea
l
-
wo
r
ld
s
ce
n
ar
io
s
.
C
o
m
p
ar
ativ
e
an
aly
s
es
o
f
SVM
r
an
k
,
GA
,
an
d
d
ee
p
lear
n
in
g
m
o
d
els
s
u
g
g
est
h
y
b
r
id
ap
p
r
o
ac
h
es
o
f
ten
y
ield
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
b
y
lev
er
ag
in
g
m
u
ltip
le
AI
tech
n
iq
u
es
[
15
].
Desp
ite
ad
v
an
ce
m
e
n
ts
,
in
teg
r
atin
g
AI
s
o
lu
tio
n
s
s
ea
m
less
ly
in
to
estab
lis
h
ed
test
in
g
wo
r
k
f
l
o
ws
r
e
m
ain
s
ch
allen
g
in
g
.
Ma
n
y
m
et
h
o
d
s
e
m
p
h
asize
e
x
ec
u
tio
n
s
p
ee
d
b
u
t
o
v
er
l
o
o
k
h
o
lis
tic
d
ef
ec
t
d
etec
tio
n
an
d
test
co
v
er
ag
e
.
Ad
d
itio
n
ally
,
t
h
e
ab
s
en
ce
o
f
s
tan
d
ar
d
ized
b
en
ch
m
ar
k
d
ata
s
ets
co
m
p
licates
p
er
f
o
r
m
an
ce
ev
alu
atio
n
ac
r
o
s
s
AI
-
d
r
iv
en
m
eth
o
d
o
lo
g
ies
[
1
6
]
.
B
u
ild
in
g
o
n
th
ese
in
s
ig
h
ts
,
th
is
r
esear
ch
p
r
esen
ts
a
h
y
b
r
i
d
AI
m
o
d
el
in
teg
r
atin
g
d
ec
is
io
n
tr
ee
s
a
n
d
GA
,
b
ala
n
cin
g
ef
f
icien
cy
,
ac
cu
r
ac
y
,
an
d
ad
ap
ta
b
ilit
y
.
E
m
p
ir
ical
v
alid
atio
n
p
r
o
v
id
es e
v
i
d
en
ce
s
u
p
p
o
r
tin
g
AI
-
p
o
wer
ed
test
p
r
io
r
itizatio
n
’
s
p
r
ac
tical
b
en
ef
its
in
s
o
f
twar
e
en
g
in
ee
r
in
g
.
2
.
2
.
Rele
a
t
ed
wo
rk
I
n
a
s
tu
d
y
b
y
[
17
]
,
r
esear
ch
e
r
s
em
p
h
asized
t
h
at
s
o
f
twar
e
q
u
ality
d
ep
en
d
s
h
ea
v
ily
o
n
t
h
e
c
o
m
p
letio
n
o
f
a
r
ig
o
r
o
u
s
test
in
g
p
r
o
ce
s
s
.
Ho
wev
er
,
test
in
g
is
r
eso
u
r
c
e
-
in
ten
s
iv
e,
o
f
ten
r
eq
u
ir
i
n
g
m
u
ltip
le
p
h
ases
th
at
p
r
o
lo
n
g
d
ev
elo
p
m
en
t
cy
cles.
T
o
ad
d
r
ess
th
ese
ch
allen
g
es,
test
ca
s
e
p
r
io
r
itizatio
n
(
T
C
P)
h
as
em
er
g
ed
as
a
cr
u
cial
s
tr
ateg
y
f
o
r
o
p
tim
izin
g
test
ex
ec
u
tio
n
.
Var
io
u
s
s
tu
d
ie
s
h
av
e
ex
p
lo
r
ed
T
C
P
’
s
ef
f
ec
tiv
en
ess
,
p
ar
ticu
lar
ly
wh
en
en
h
an
ce
d
b
y
ML
tech
n
iq
u
es
s
u
ch
as
SVMs
,
n
eu
r
a
l
n
etwo
r
k
s
,
an
d
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
,
wh
ic
h
au
to
m
ate
test
s
elec
tio
n
an
d
im
p
r
o
v
e
e
x
ec
u
tio
n
ef
f
icien
c
y
.
I
n
a
n
o
t
h
e
r
s
t
u
d
y
,
[
18
]
i
n
t
r
o
d
u
c
e
d
t
h
e
c
o
m
p
l
i
c
a
t
e
d
o
b
j
e
c
t
g
e
n
e
r
a
t
i
o
n
(
C
O
G
)
te
c
h
n
i
q
u
e
,
a
s
e
m
i
-
a
u
t
o
m
a
t
e
d
a
p
p
r
o
a
c
h
d
e
s
i
g
n
ed
t
o
g
e
n
e
r
a
t
e
c
o
m
p
l
e
x
cl
a
s
s
i
n
s
t
a
n
c
es
f
o
r
b
l
a
c
k
-
b
o
x
t
est
i
n
g
i
n
J
a
v
a
-
b
as
e
d
a
p
p
l
i
c
a
t
i
o
n
s
.
W
h
i
l
e
C
OG
f
u
n
ct
i
o
n
s
e
f
f
e
ct
i
v
e
l
y
at
t
h
e
u
n
i
t
t
es
t
i
n
g
l
e
v
el
,
i
ts
a
p
p
li
c
a
b
i
li
t
y
t
o
f
u
l
l
-
s
y
s
t
e
m
t
es
ti
n
g
p
r
e
s
e
n
t
s
s
c
al
a
b
i
li
t
y
is
s
u
es
d
u
e
t
o
t
h
e
h
i
g
h
v
o
l
u
m
e
o
f
t
e
s
t
d
a
t
a
.
T
C
P
p
l
a
y
s
a
c
r
i
ti
c
a
l
r
o
l
e
i
n
a
d
d
r
e
s
s
i
n
g
t
h
e
s
e
c
o
n
s
t
r
a
i
n
ts
,
e
n
s
u
r
i
n
g
t
h
a
t
es
s
en
t
i
a
l
c
as
e
s
a
r
e
e
x
e
c
u
te
d
f
i
r
s
t
wh
i
l
e
r
e
d
u
n
d
a
n
t
c
a
s
e
s
a
r
e
m
i
n
im
i
z
e
d
.
S
o
m
e
s
t
u
d
i
e
s
p
r
o
p
o
s
e
s
e
m
i
-
s
u
p
e
r
v
i
s
e
d
a
n
d
u
n
s
u
p
e
r
v
i
s
e
d
le
a
r
n
i
n
g
a
s
p
o
t
e
n
t
i
a
l
s
o
l
u
ti
o
n
s
t
o
r
e
d
u
c
e
d
ep
e
n
d
e
n
c
y
o
n
l
a
r
g
e
,
l
a
b
e
l
e
d
d
a
t
as
e
ts
,
m
a
k
i
n
g
M
L
-
b
a
s
e
d
t
es
t
i
n
g
m
et
h
o
d
o
l
o
g
i
e
s
m
o
r
e
a
d
a
p
t
a
b
l
e
t
o
r
e
al
-
w
o
r
l
d
a
p
p
l
ic
a
t
i
o
n
s
.
A
d
if
f
er
en
t
ap
p
r
o
ac
h
was
ex
p
lo
r
ed
b
y
[
19
]
,
wh
er
e
a
m
etad
ata
-
d
r
iv
e
n
p
r
io
r
itizatio
n
tech
n
iq
u
e
was
d
ev
elo
p
e
d
f
o
r
m
an
u
ally
ex
ec
u
ted
test
ca
s
es.
T
h
is
m
et
h
o
d
u
t
ilized
n
atu
r
al
lan
g
u
ag
e
ar
tifa
c
ts
an
d
m
etad
ata
to
co
m
p
u
te
test
p
r
io
r
ity
v
alu
es.
T
h
e
tech
n
iq
u
e
was
ev
alu
ated
u
s
in
g
th
r
ee
r
ea
l
-
wo
r
ld
r
e
g
r
ess
io
n
test
in
g
d
atasets
f
r
o
m
th
e
au
to
m
o
tiv
e
in
d
u
s
tr
y
,
d
em
o
n
s
tr
atin
g
th
a
t
ML
-
b
ased
ap
p
r
o
ac
h
es
ca
n
s
ig
n
if
ican
tly
en
h
an
ce
b
lack
-
b
o
x
test
in
g
.
Fin
d
in
g
s
s
u
g
g
est
th
at
h
y
b
r
id
m
eth
o
d
o
l
o
g
ies
—
lev
er
ag
in
g
SVM
r
an
k
,
GA
,
an
d
d
ee
p
lear
n
in
g
m
o
d
els
—
o
f
ten
o
u
tp
er
f
o
r
m
s
in
g
le
-
m
o
d
el
a
p
p
r
o
ac
h
es,
ef
f
ec
ti
v
ely
b
alan
cin
g
s
p
ee
d
,
ac
c
u
r
ac
y
,
an
d
ad
ap
tab
ilit
y.
Me
an
wh
ile,
[
2
0
]
in
v
esti
g
ated
an
ev
o
lu
tio
n
a
r
y
p
a
r
ad
ig
m
f
o
r
wh
ite
-
b
o
x
test
in
g
,
in
teg
r
at
in
g
GA
to
au
to
m
ate
test
d
ata
g
en
er
atio
n
wh
ile
en
s
u
r
in
g
m
a
x
im
u
m
s
t
atem
en
t
co
v
er
a
g
e.
T
h
eir
a
p
p
r
o
ac
h
s
u
cc
ess
f
u
lly
ac
h
iev
ed
1
0
0
%
s
tatem
en
t
co
v
er
ag
e
in
a
s
in
g
le
GA
ex
ec
u
tio
n
,
s
h
o
wca
s
in
g
its
ef
f
icien
c
y
.
Ho
wev
er
,
wh
ile
th
is
m
eth
o
d
ac
ce
ler
ates
test
in
g
,
it
d
o
es
n
o
t
co
m
p
r
e
h
en
s
iv
ely
a
d
d
r
ess
h
o
lis
tic
d
ef
ec
t
d
etec
tio
n
an
d
o
v
er
all
test
co
v
er
ag
e.
Fu
r
th
e
r
m
o
r
e
,
b
en
c
h
m
ar
k
d
atasets
r
em
ain
s
ca
r
c
e,
m
ak
in
g
it
d
if
f
ic
u
lt
to
estab
lis
h
s
tan
d
ar
d
ized
p
er
f
o
r
m
an
ce
ev
al
u
atio
n
s
ac
r
o
s
s
d
if
f
er
en
t A
I
-
d
r
iv
en
test
in
g
m
eth
o
d
o
lo
g
ies.
A
b
r
o
ad
liter
atu
r
e
r
ev
iew
co
n
d
u
cted
b
y
[
21
]
f
u
r
th
er
e
x
am
i
n
ed
th
e
in
teg
r
atio
n
o
f
ML
in
au
to
m
ated
test
ca
s
e
g
en
er
atio
n
,
an
aly
zin
g
9
7
p
u
b
licatio
n
s
.
T
h
e
f
in
d
i
n
g
s
co
n
f
i
r
m
ed
th
at
ML
en
h
an
ce
s
ex
is
tin
g
test
in
g
tech
n
iq
u
es,
im
p
r
o
v
in
g
test
in
p
u
t
g
en
er
atio
n
,
s
y
s
tem
v
alid
atio
n
,
GUI
test
in
g
,
an
d
co
m
b
in
ato
r
ial
test
ca
s
e
s
elec
tio
n
.
C
o
m
m
o
n
ly
ap
p
lied
ML
tech
n
iq
u
es
in
clu
d
e
s
u
p
er
v
is
ed
lear
n
in
g
(
o
f
ten
n
eu
r
al
n
etwo
r
k
-
b
ased
)
,
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
(
f
r
e
q
u
en
tly
Q
-
lear
n
in
g
-
b
ased
)
,
a
n
d
s
em
i
-
s
u
p
er
v
is
ed
lear
n
in
g
.
Ho
w
ev
er
,
th
e
s
tu
d
y
also
h
ig
h
lig
h
ted
p
e
r
s
is
ten
t
ch
allen
g
es
s
u
ch
as
d
ata
a
v
ailab
ilit
y
,
s
ca
lab
ilit
y
,
m
o
d
el
r
etr
ain
in
g
c
o
m
p
lex
ity
,
an
d
th
e
lack
o
f
s
tan
d
ar
d
ize
d
ML
test
in
g
b
en
c
h
m
ar
k
s
.
Desp
ite
th
ese
lim
itatio
n
s
,
AI
-
d
r
iv
en
T
C
P
r
em
ain
s
a
p
r
o
m
is
in
g
av
en
u
e,
o
f
f
er
i
n
g
im
p
r
o
v
em
en
ts
in
ef
f
icien
cy
,
ac
cu
r
ac
y
,
a
n
d
ad
ap
tab
ilit
y
.
B
y
v
alid
atin
g
em
p
ir
ical
f
in
d
in
g
s
,
th
is
r
esear
c
h
aim
s
to
p
r
o
v
id
e
q
u
a
n
tifia
b
le
e
v
id
en
ce
o
f
AI
-
p
o
wer
ed
test
p
r
io
r
itizatio
n
’
s
p
r
ac
tical
b
en
e
f
its
in
m
o
d
er
n
s
o
f
twar
e
en
g
i
n
ee
r
in
g
.
3.
CO
NCEPT
UAL AP
P
RO
A
C
H
VIE
WP
O
I
NT
T
h
e
th
eo
r
etica
l
f
r
am
ew
o
r
k
d
er
iv
ed
f
r
o
m
p
r
io
r
r
esear
ch
c
o
v
er
s
a
d
iv
er
s
e
s
et
o
f
m
eth
o
d
o
lo
g
ies
in
so
f
twar
e
test
in
g
.
I
t
h
ig
h
lig
h
ts
k
ey
tech
n
iq
u
es
s
u
ch
as
b
o
u
n
d
ar
y
v
alu
e
an
aly
s
is
(
B
VA)
,
d
y
n
am
ic
ex
ec
u
tio
n
-
b
ased
test
in
g
,
ML
-
d
r
iv
e
n
T
C
P
,
an
d
th
e
in
teg
r
atio
n
o
f
GA
with
b
in
ar
y
s
ea
r
ch
m
eth
o
d
s
.
T
h
ese
m
eth
o
d
o
l
o
g
ies
aim
to
en
h
a
n
ce
b
o
t
h
th
e
ef
f
icien
cy
an
d
ef
f
ec
tiv
e
n
e
s
s
o
f
th
e
s
o
f
twar
e
test
in
g
life
cy
cle.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
E
xp
lo
r
in
g
d
ivers
e
p
ers
p
ec
tives:
en
h
a
n
cin
g
b
la
ck
b
o
x
test
in
g
th
r
o
u
g
h
m
a
ch
in
e
…
(
Heb
a
N
a
fez Ja
la
l
)
241
A
g
e
n
e
r
a
l
c
o
n
s
e
n
s
u
s
e
x
i
s
t
s
a
m
o
n
g
r
e
s
e
a
r
c
h
e
r
s
o
n
t
h
e
s
i
g
n
i
f
i
c
a
n
c
e
o
f
c
o
m
b
i
n
i
n
g
m
u
l
t
i
p
l
e
m
e
t
h
o
d
o
l
o
g
i
e
s
f
o
r
im
p
r
o
v
e
d
test
in
g
o
u
tco
m
es.
Stu
d
ies
h
av
e
d
em
o
n
s
tr
ate
d
th
at
m
ac
h
i
n
e
-
lear
n
i
n
g
-
d
r
iv
e
n
p
r
io
r
itizatio
n
an
d
SVM
r
an
k
-
b
ased
test
ca
s
e
s
el
ec
tio
n
en
h
an
ce
test
ef
f
icien
cy
b
y
s
y
s
tem
atica
lly
f
ilter
in
g
r
ed
u
n
d
a
n
t
ca
s
es
an
d
p
r
io
r
itizin
g
h
i
g
h
-
r
is
k
test
s
ce
n
ar
io
s
[
22
]
.
Ho
wev
er
,
d
is
ag
r
ee
m
en
ts
p
er
s
is
t
r
eg
ar
d
in
g
th
e
f
ea
s
ib
ilit
y
an
d
ad
ap
tab
ilit
y
o
f
s
o
m
e
tec
h
n
iq
u
es
in
h
an
d
lin
g
co
m
p
l
e
x
d
at
asets
an
d
r
ea
l
-
wo
r
ld
test
in
g
en
v
ir
o
n
m
en
ts
.
Fo
r
in
s
tan
ce
,
s
o
m
e
r
esear
ch
er
s
s
u
p
p
o
r
t
th
e
ap
p
licatio
n
o
f
C
OG
as
a
v
iab
le
T
C
P
m
eth
o
d
.
Ho
wev
er
,
o
th
er
s
q
u
esti
o
n
its
s
ca
lab
ilit
y
wh
en
a
p
p
lied
to
lar
g
e
-
s
ca
le
d
atasets
,
p
ar
ticu
lar
ly
in
b
ig
d
ata
en
v
ir
o
n
m
en
ts
.
Sim
ilar
ly
,
MSB
VM
-
b
ased
m
eth
o
d
o
lo
g
ie
s
h
av
e
f
ac
ed
ch
allen
g
es in
ef
f
ec
tiv
ely
m
an
ag
in
g
s
p
ec
ialized
d
atasets
,
wh
ich
h
as
led
to
ca
lls
f
o
r
f
u
r
th
er
r
ef
in
em
en
t a
n
d
a
d
ap
tatio
n
[
23
].
Mo
r
eo
v
er
,
r
esear
ch
v
ar
ies
in
s
co
p
e
an
d
d
e
p
th
.
W
h
ile
s
o
m
e
s
tu
d
ies
p
r
o
v
id
e
b
r
o
ad
th
e
o
r
eti
ca
l
in
s
ig
h
ts
in
to
test
in
g
m
eth
o
d
o
l
o
g
ies,
o
th
er
s
f
o
cu
s
o
n
r
ea
l
-
w
o
r
ld
ap
p
licatio
n
s
s
u
ch
as
lear
n
in
g
m
an
ag
em
en
t
s
y
s
tem
s
(
L
MS)
an
d
en
ter
p
r
is
e
s
o
f
twar
e
p
latf
o
r
m
s
.
T
h
ese
p
r
ac
tical
im
p
lem
en
tatio
n
s
s
h
o
wca
s
e
t
h
e
d
ir
ec
t
im
p
ac
t
o
f
v
ar
io
u
s
p
r
i
o
r
itizatio
n
tech
n
i
q
u
es o
n
r
ea
l
-
tim
e
s
o
f
twar
e
p
er
f
o
r
m
an
ce
an
d
d
ef
ec
t
d
etec
tio
n
.
C
o
n
ce
p
t
u
a
l
ap
p
r
o
ac
h
f
r
a
m
e
wo
r
k
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
f
o
ll
o
ws
a
s
tr
u
ctu
r
ed
wo
r
k
f
lo
w
f
o
r
T
C
P
in
s
o
f
twar
e
test
in
g
.
Fig
u
r
e
2
illu
s
tr
ates th
is
p
r
o
ce
s
s
,
wh
ich
co
n
s
is
ts
o
f
m
u
ltip
le
s
tag
es:
a)
Data
co
llectio
n
p
h
ase
:
−
R
elev
an
t te
s
t d
ata
is
g
ath
er
ed
f
o
r
an
al
y
s
is
an
d
p
r
io
r
itizatio
n
.
−
E
n
s
u
r
es in
clu
s
io
n
o
f
ess
en
tial te
s
tin
g
attr
ib
u
tes.
b)
Data
p
r
ep
r
o
ce
s
s
in
g
p
h
ase
:
−
C
lean
s
an
d
n
o
r
m
alize
s
d
ata
to
en
s
u
r
e
co
n
s
is
ten
cy
.
−
R
em
o
v
es r
ed
u
n
d
an
t a
n
d
lo
w
-
i
m
p
ac
t te
s
t
ca
s
es.
c)
B
r
an
ch
A:
ML
-
d
r
iv
e
n
p
r
i
o
r
itizatio
n
:
−
ML
m
o
d
els (
e.
g
.
,
d
ec
is
io
n
tr
ee
s
,
n
eu
r
al
n
etwo
r
k
s
)
an
aly
ze
t
h
e
p
r
ep
r
o
ce
s
s
ed
d
ataset.
−
Patter
n
s
an
d
in
s
ig
h
ts
ar
e
ex
tr
a
cted
to
p
r
io
r
itize
test
ca
s
es b
as
ed
o
n
p
o
ten
tial d
ef
ec
t
id
en
tific
atio
n
.
d)
B
r
an
ch
B
: SVM
r
an
k
f
o
r
T
C
P
−
SVM
r
an
k
alg
o
r
ith
m
ass
ig
n
s
r
an
k
in
g
s
co
r
es to
test
ca
s
es.
−
Hig
h
er
-
r
an
k
ed
test
ca
s
es a
r
e
p
r
io
r
itized
b
ased
o
n
d
e
f
ec
t lik
elih
o
o
d
.
e)
E
v
alu
atio
n
an
d
co
m
p
ar
is
o
n
p
h
ase
:
−
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
b
o
th
p
r
io
r
itizatio
n
tech
n
iq
u
es is
ass
ess
e
d
.
−
Me
tr
ics s
u
ch
as a
cc
u
r
ac
y
,
r
ec
a
ll,
an
d
p
r
ec
is
io
n
ar
e
co
m
p
u
ted
[
2
4
]
.
f)
Per
f
o
r
m
an
ce
ass
ess
m
en
t p
h
ase:
−
T
h
e
ef
f
ec
tiv
en
ess
o
f
ea
ch
ap
p
r
o
ac
h
is
an
aly
ze
d
.
−
T
h
e
p
r
io
r
itizatio
n
m
o
d
el
th
a
t
o
f
f
e
r
s
o
p
tim
al
s
o
f
twar
e
d
e
f
ec
t
d
etec
tio
n
an
d
e
x
ec
u
tio
n
ef
f
icien
c
y
is
s
elec
ted
[
2
5
]
.
Fig
u
r
e
2
.
C
o
n
ce
p
tu
al
ap
p
r
o
ac
h
v
iewp
o
in
T
h
is
co
m
p
ar
ativ
e
a
n
aly
s
is
o
f
f
er
s
d
ee
p
er
in
s
ig
h
ts
in
to
th
e
s
tr
en
g
th
s
an
d
wea
k
n
ess
es
o
f
ea
ch
m
eth
o
d
,
aid
in
g
in
th
e
s
elec
tio
n
o
f
th
e
m
o
s
t
s
u
itab
le
p
r
io
r
itizatio
n
ap
p
r
o
ac
h
f
o
r
a
g
iv
en
s
o
f
t
war
e
en
v
ir
o
n
m
en
t.
Ultim
ately
,
th
e
p
r
o
ce
s
s
aim
s
to
en
s
u
r
e
th
at
s
o
f
twar
e
test
in
g
i
s
b
o
th
ef
f
ec
tiv
e
a
n
d
r
eso
u
r
ce
-
e
f
f
icien
t,
en
h
a
n
cin
g
o
v
er
all
s
o
f
twar
e
q
u
ality
an
d
r
e
liab
ilit
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
1
,
Ma
r
ch
20
26
:
23
8
-
24
6
242
4.
O
P
E
RAT
I
O
NAL
AP
P
RO
A
CH
VIE
WP
O
I
NT
T
h
e
o
p
e
r
a
t
i
o
n
a
l
f
r
a
m
e
w
o
r
k
o
f
t
h
i
s
r
e
s
e
a
r
c
h
i
s
d
e
s
i
g
n
e
d
t
o
o
p
t
i
m
i
z
e
T
C
P
b
y
e
m
p
l
o
y
i
n
g
ML
m
o
d
e
l
s
a
n
d
r
a
n
k
i
n
g
-
b
a
s
e
d
a
p
p
r
o
a
c
h
e
s
.
T
h
i
s
s
e
c
ti
o
n
d
e
t
a
i
ls
t
h
e
k
e
y
p
h
a
s
e
s
i
n
v
o
l
v
e
d
,
p
r
o
v
i
d
i
n
g
a
s
t
r
u
c
t
u
r
e
d
w
o
r
k
f
l
o
w
f
o
r
e
f
f
i
c
i
e
n
t
te
s
t
c
a
s
e
s
el
e
c
ti
o
n
,
e
v
a
l
u
a
t
i
o
n
,
a
n
d
p
e
r
f
o
r
m
a
n
c
e
c
o
m
p
a
r
i
s
o
n
.
4
.
1
.
Da
t
a
prepro
ce
s
s
ing
ph
a
s
e
T
h
e
p
r
o
ce
s
s
b
eg
in
s
with
d
ata
co
llectio
n
an
d
p
r
ep
r
o
ce
s
s
in
g
,
en
s
u
r
in
g
th
at
o
n
ly
h
ig
h
-
q
u
alit
y
,
r
elev
an
t
d
ata
is
u
s
ed
f
o
r
p
r
io
r
itizatio
n
.
Fig
u
r
e
3
illu
s
tr
ates th
is
s
tag
e,
wh
er
e
r
aw
test
ca
s
e
d
ata
u
n
d
er
g
o
es p
r
ep
r
o
ce
s
s
in
g
task
s
s
u
ch
as:
−
Data
clea
n
in
g
: r
em
o
v
in
g
d
u
p
li
ca
te
o
r
ir
r
elev
a
n
t te
s
t c
ases
.
−
No
r
m
aliza
tio
n
:
e
n
s
u
r
in
g
d
ata
co
n
s
is
ten
cy
f
o
r
ML
m
o
d
els.
−
Featu
r
e
ex
tr
ac
tio
n
: id
e
n
tify
in
g
th
e
m
o
s
t r
elev
an
t a
ttri
b
u
tes f
o
r
T
C
P
.
T
h
is
s
tep
is
ess
en
tial
in
r
ed
u
ci
n
g
r
ed
u
n
d
an
c
y
a
n
d
en
s
u
r
in
g
t
h
at
ML
m
o
d
els r
ec
eiv
e
s
tr
u
ctu
r
ed
a
n
d
m
ea
n
in
g
f
u
l
in
p
u
t d
ata
f
o
r
f
u
r
th
er
p
r
o
ce
s
s
in
g
.
Fig
u
r
e
3
.
Pre
p
r
o
ce
s
s
ed
d
ata
to
p
r
io
r
itize
test
ca
s
es
4
.
2
.
ML
-
driv
en
T
CP
(
B
ra
nc
h A)
Af
ter
p
r
ep
r
o
ce
s
s
in
g
,
th
e
wo
r
k
f
lo
w
d
iv
er
g
es
in
to
two
p
a
r
allel
p
r
io
r
itizatio
n
p
ath
s
.
T
h
e
f
i
r
s
t
ap
p
r
o
ac
h
u
tili
ze
s
ML
m
o
d
els
(
e.
g
.
,
d
ec
is
io
n
tr
ee
s
,
n
eu
r
al
n
etwo
r
k
s
)
t
o
class
if
y
test
ca
s
es
b
ased
o
n
th
eir
lik
elih
o
o
d
o
f
d
etec
tin
g
s
o
f
twar
e
d
ef
ec
ts
.
T
h
e
alg
o
r
ith
m
a
s
s
ig
n
s
p
r
io
r
ity
s
co
r
es
b
y
a
n
aly
zin
g
p
atter
n
s
with
in
th
e
d
ataset,
id
en
tify
in
g
h
ig
h
-
r
is
k
ar
ea
s
th
a
t r
eq
u
ir
e
im
m
e
d
iate
test
in
g
.
T
h
e
ad
v
a
n
tag
es o
f
th
is
ap
p
r
o
a
ch
in
clu
d
e:
−
I
m
p
r
o
v
ed
a
d
ap
tab
ilit
y
:
c
an
h
a
n
d
le
d
y
n
am
ic
test
en
v
ir
o
n
m
en
ts
ef
f
icien
tly
.
−
Au
to
m
ated
p
r
i
o
r
it
izatio
n
:
r
ed
u
ce
s
r
elian
ce
o
n
m
a
n
u
al
test
s
elec
tio
n
.
−
E
n
h
an
ce
d
d
ef
ec
t
d
etec
tio
n
r
at
es:
i
d
en
tifie
s
h
ig
h
-
r
is
k
test
ca
s
es m
o
r
e
ef
f
ec
tiv
ely
.
4
.
3
.
SVM
ra
nk
-
ba
s
ed
T
CP
(
B
ra
nch B
)
T
h
e
s
ec
o
n
d
p
r
io
r
itizatio
n
p
at
h
em
p
l
o
y
s
SVM
r
a
n
k
,
a
ML
tech
n
iq
u
e
th
at
ass
ig
n
s
r
a
n
k
in
g
s
co
r
es
t
o
test
ca
s
es
b
ased
o
n
th
eir
r
elev
an
ce
to
th
e
s
o
f
twar
e
u
n
d
er
test
.
As
s
h
o
wn
i
n
Fig
u
r
e
4
,
th
is
m
eth
o
d
en
s
u
r
es
th
a
t
test
ca
s
es a
r
e
r
an
k
ed
d
y
n
am
ic
ally
,
allo
win
g
f
o
r
a
s
tr
u
ctu
r
e
d
p
r
io
r
itizatio
n
p
r
o
ce
s
s
.
T
h
e
Key
ad
v
an
tag
es o
f
SVM
r
an
k
:
−
Ma
th
em
atica
lly
o
p
tim
ized
r
a
n
k
in
g
:
u
s
es a
d
v
an
ce
d
r
a
n
k
in
g
m
o
d
els to
ass
ig
n
im
p
o
r
tan
ce
l
ev
els.
−
Scalab
ilit
y
:
p
er
f
o
r
m
s
ef
f
icien
tl
y
ev
en
i
n
lar
g
e
-
s
ca
le
test
in
g
e
n
v
ir
o
n
m
en
ts
.
−
I
m
p
r
o
v
ed
r
eso
u
r
ce
all
o
ca
tio
n
:
e
n
s
u
r
es th
at
th
e
h
ig
h
est
-
p
r
io
r
it
y
test
ca
s
es a
r
e
ex
ec
u
ted
f
ir
s
t.
Fig
u
r
e
4
.
SVM
r
an
k
tech
n
i
q
u
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
E
xp
lo
r
in
g
d
ivers
e
p
ers
p
ec
tives:
en
h
a
n
cin
g
b
la
ck
b
o
x
test
in
g
th
r
o
u
g
h
m
a
ch
in
e
…
(
Heb
a
N
a
fez Ja
la
l
)
243
4
.
4
.
Co
m
pa
ra
t
iv
e
ev
a
lua
t
io
n a
nd
perf
o
rm
a
nce
a
s
s
ess
m
ent
T
o
d
eter
m
i
n
e
th
e
ef
f
ec
tiv
en
es
s
o
f
b
o
th
p
r
io
r
itizatio
n
m
eth
o
d
s
,
th
e
ev
al
u
atio
n
a
n
d
c
o
m
p
ar
is
o
n
p
h
ase
ass
es
s
es th
eir
p
er
f
o
r
m
an
ce
b
ased
o
n
:
−
Acc
u
r
ac
y
:
h
o
w
p
r
ec
is
ely
ea
ch
m
eth
o
d
p
r
io
r
itizes test
ca
s
es.
−
E
x
ec
u
tio
n
e
f
f
icien
cy
: th
e
tim
e
r
eq
u
ir
e
d
f
o
r
test
ca
s
e
s
elec
tio
n
an
d
e
x
ec
u
tio
n
.
−
Def
ec
t
d
etec
tio
n
r
ate:
th
e
p
er
c
en
tag
e
o
f
d
ef
ec
ts
id
en
tifie
d
u
s
in
g
ea
ch
m
eth
o
d
.
E
m
p
ir
ical
r
esu
lts
d
em
o
n
s
tr
ate
th
at
ML
-
d
r
iv
en
p
r
i
o
r
itizatio
n
ac
h
iev
es h
ig
h
er
ac
cu
r
ac
y
,
wh
e
r
ea
s
SV
M
r
an
k
e
x
ce
ls
in
s
tr
u
ctu
r
ed
r
an
k
in
g
a
n
d
c
o
m
p
u
tatio
n
al
ef
f
icien
cy
.
T
h
e
f
in
al
p
er
f
o
r
m
a
n
ce
ass
ess
m
en
t
p
h
ase
in
v
o
lv
es
s
elec
tin
g
th
e
ap
p
r
o
ac
h
th
at
b
est
alig
n
s
with
th
e
g
iv
en
s
o
f
twar
e
en
v
ir
o
n
m
en
t
an
d
test
in
g
r
eq
u
ir
em
e
n
ts
.
T
h
e
o
p
er
atio
n
al
f
r
am
ewo
r
k
o
u
tlin
ed
in
th
is
r
esear
ch
p
r
o
v
id
es
a
s
tr
u
ctu
r
ed
ap
p
r
o
ac
h
to
T
C
P
,
lev
er
ag
in
g
ML
-
d
r
iv
e
n
m
o
d
el
s
an
d
r
an
k
in
g
-
b
ased
tech
n
iq
u
es.
B
y
co
m
p
ar
in
g
th
e
ef
f
ec
tiv
en
ess
o
f
d
ec
is
io
n
tr
ee
-
b
ased
p
r
i
o
r
itizatio
n
an
d
S
VM
r
an
k
-
b
ased
r
an
k
in
g
,
th
is
s
tu
d
y
d
e
m
o
n
s
tr
ates
h
o
w
AI
-
d
r
i
v
en
m
eth
o
d
o
lo
g
ies
ca
n
s
ig
n
if
ican
tly
en
h
an
ce
s
o
f
twar
e
test
in
g
p
r
o
ce
s
s
es.
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v
in
g
f
o
r
war
d
,
th
e
in
teg
r
atio
n
o
f
m
o
r
e
ad
v
a
n
ce
d
AI
tech
n
i
q
u
es
will
b
e
ess
en
tial
in
f
u
r
th
er
r
ef
in
in
g
test
ca
s
e
s
elec
tio
n
,
m
ax
im
izin
g
ef
f
icie
n
cy
,
an
d
en
s
u
r
in
g
s
o
f
twar
e
q
u
ality
.
5.
RE
SU
L
T
S a
nd
DI
SCU
SS
I
O
N
5
.
1
.
E
x
perim
ent
a
l
s
et
up
T
o
ass
ess
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
p
r
o
p
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ed
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P
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p
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h
,
ex
p
er
im
e
n
ts
wer
e
co
n
d
u
ct
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u
s
i
n
g
a
r
ea
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ld
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o
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at
aset
o
f
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0
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0
0
0
test
ca
s
es
lab
el
ed
with
d
ef
ec
t
s
ev
er
ity
le
v
els.
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h
e
s
etu
p
in
clu
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ed
a
h
ig
h
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p
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o
r
m
a
n
ce
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m
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u
ti
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g
en
v
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en
t
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n
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o
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th
o
n
-
b
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AI
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r
am
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k
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.
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h
e
s
tu
d
y
co
m
p
ar
ed
ML
-
d
r
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v
en
p
r
io
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n
(
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r
an
ch
A)
an
d
SVM
r
an
k
-
b
ased
p
r
i
o
r
it
izatio
n
(
B
r
an
ch
B
)
with
tr
ad
itio
n
al
r
an
d
o
m
an
d
s
eq
u
en
tial
test
ex
ec
u
tio
n
s
tr
ate
g
ies.
Per
f
o
r
m
an
ce
was
ev
alu
a
ted
u
s
in
g
ac
cu
r
ac
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,
p
r
ec
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io
n
,
r
ec
all,
ex
e
cu
tio
n
ti
m
e,
an
d
d
ef
ec
t d
etec
tio
n
r
ate.
5
.
2
.
P
er
f
o
r
m
a
nce
co
m
pa
riso
n
T
h
e
r
esu
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in
Fig
u
r
e
5
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ig
h
li
g
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th
e
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er
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a
n
ce
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u
p
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r
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ty
o
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m
eth
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s
co
m
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r
ed
to
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al
ap
p
r
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h
es.
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b
ased
p
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io
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itizatio
n
ac
h
i
ev
ed
th
e
h
ig
h
est
ac
cu
r
ac
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(
9
6
.
0
%)
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d
d
ef
ec
t
d
etec
tio
n
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ate
(
9
0
.
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%),
s
ig
n
if
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ca
n
tly
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ed
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ci
n
g
e
x
ec
u
tio
n
tim
e
to
8
0
s
ec
o
n
d
s
.
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an
k
-
b
ased
p
r
io
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itizatio
n
p
er
f
o
r
m
ed
s
lig
h
tly
lo
wer
b
u
t sti
ll e
x
h
ib
ited
s
u
b
s
tan
tial im
p
r
o
v
em
en
ts
o
v
er
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n
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en
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al
m
e
th
o
d
s
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4
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r
ac
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d
an
8
8
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5
% d
ef
ec
t
d
etec
tio
n
r
ate.
Fig
u
r
e
5
.
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f
o
r
m
an
c
e
co
m
p
ar
is
o
n
o
f
T
C
P
m
eth
o
d
s
I
n
co
n
tr
ast,
tr
ad
itio
n
al
m
eth
o
d
s
—
r
an
d
o
m
s
elec
tio
n
an
d
s
eq
u
en
tial
ex
ec
u
tio
n
—
s
tr
u
g
g
le
d
with
lo
wer
ac
cu
r
ac
y
an
d
lo
n
g
er
ex
ec
u
tio
n
tim
es.
R
an
d
o
m
test
s
elec
ti
o
n
h
ad
t
h
e
lo
west
d
ef
ec
t
d
et
ec
tio
n
r
ate
(
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5
.
1
%),
em
p
h
asizin
g
its
in
e
f
f
icien
cy
in
p
r
i
o
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itizin
g
c
r
itical
test
ca
s
es.
Seq
u
en
tial
ex
ec
u
tio
n
p
er
f
o
r
m
ed
b
etter
th
an
r
an
d
o
m
s
elec
tio
n
b
u
t r
e
m
ain
e
d
in
f
er
io
r
to
AI
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b
ased
m
eth
o
d
s
in
b
o
th
ef
f
icien
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an
d
ef
f
ec
ti
v
en
ess
.
T
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e
s
e
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i
n
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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Vo
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Ma
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20
26
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244
6.
CO
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