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io
na
l J
o
urna
l o
f
E
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m
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ng
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I
J
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)
Vo
l.
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~
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I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
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1
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v
1
6
i
4
.
pp
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20
30
2014
J
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ttp
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CC B
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C
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id
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I
NT
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UCT
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O
N
So
f
twar
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test
in
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an
es
s
en
t
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co
m
p
o
n
en
t
in
th
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o
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ality
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alit
y
th
r
o
u
g
h
ea
r
ly
b
u
g
d
etec
tio
n
[
1
]
.
Var
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u
s
em
p
ir
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s
tu
d
ies
r
ep
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r
t
th
at
b
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co
m
p
atib
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y
is
s
u
es,
an
d
r
eliab
ilit
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is
s
u
es
[
2
]
,
wh
ile
in
in
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u
s
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s
y
s
tem
s
,
in
ter
f
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r
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s
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te
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[
3
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.
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esti
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ality
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n
th
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test
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in
id
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u
s
u
ally
f
ail
to
u
n
co
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id
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en
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[
4
]
.
On
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allen
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test
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tech
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atasets
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ex
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f
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p
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r
am
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s
[
5
]
.
R
ea
lis
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b
u
g
s
a
r
e
b
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t
h
a
t
n
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ally
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cc
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r
in
th
e
s
o
f
twar
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t
p
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s
.
T
h
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s
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f
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lis
tic
b
u
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in
th
e
s
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f
twar
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test
in
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lear
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in
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p
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s
s
ca
n
im
p
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v
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test
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s
'
an
aly
tical
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d
in
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itiv
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a
b
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in
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esig
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in
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test
ca
s
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ca
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ab
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a
n
m
er
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s
im
p
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s
y
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tax
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s
[
5
]
,
[
6
]
.
R
ea
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ex
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it
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ig
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co
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p
lex
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lect
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p
lex
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in
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m
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in
g
th
e
ev
alu
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n
o
f
test
in
g
tech
n
iq
u
es
[
7
]
,
[
8
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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p
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I
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N:
2088
-
8
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9
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1
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1
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ltin
g
m
u
tan
ts
ar
e
o
f
ten
ir
r
elev
a
n
t
to
th
e
a
p
p
licatio
n
'
s
b
u
s
in
ess
lo
g
ic
an
d
th
er
e
f
o
r
e
d
o
n
o
t
r
ef
lect
r
ea
lis
tic
b
u
g
s
,
b
ec
au
s
e
m
u
tatio
n
s
ar
e
p
er
f
o
r
m
e
d
r
an
d
o
m
ly
with
o
u
t
co
n
s
id
er
in
g
th
e
r
eq
u
ir
em
e
n
ts
'
co
n
tex
t.
Mu
tati
o
n
o
p
er
ato
r
s
o
n
ly
m
o
d
if
y
p
r
o
g
r
am
c
o
d
e
i
n
a
s
in
g
le
ex
p
r
ess
io
n
in
a
s
in
g
le
lin
e,
s
o
m
u
tan
ts
d
o
n
o
t
co
v
e
r
m
u
lti
-
lin
e
b
u
g
s
th
at
c
o
m
m
o
n
ly
ar
is
e
d
u
e
to
f
u
n
ctio
n
ality
e
r
r
o
r
s
.
On
th
e
o
th
er
h
an
d
,
m
an
y
m
u
tan
ts
lead
to
th
e
m
u
tan
t
ex
p
lo
s
io
n
p
r
o
b
lem
,
in
w
h
ich
m
u
ta
n
ts
ar
e
d
if
f
icu
lt
to
co
n
tr
o
l,
r
esu
ltin
g
i
n
lo
n
g
a
n
aly
s
is
tim
es
[
1
0
]
.
Me
a
n
wh
ile,
p
r
o
g
r
a
m
er
r
o
r
s
d
o
n
o
t
alwa
y
s
o
r
ig
i
n
ate
f
r
o
m
s
y
n
tact
ic
er
r
o
r
s
;
b
u
g
s
ca
n
also
ar
is
e
f
r
o
m
r
eq
u
ir
e
m
en
t
l
ev
el
f
au
lts
,
wh
ich
ar
e
ca
u
s
ed
b
y
m
is
in
ter
p
r
etin
g
s
y
s
tem
r
e
q
u
ir
em
en
ts
d
u
r
in
g
im
p
lem
en
tatio
n
[
1
3
]
.
T
h
is
ty
p
e
o
f
er
r
o
r
ca
u
s
es
th
e
s
y
s
tem
'
s
b
eh
av
io
r
n
o
t
to
m
atch
th
e
r
eq
u
ir
em
en
ts
.
Fo
r
ex
am
p
le,
th
e
l
o
g
in
f
u
n
ctio
n
f
a
ils
to
v
alid
ate
u
s
er
r
o
le
co
m
b
i
n
atio
n
s
ac
co
r
d
i
n
g
to
b
u
s
in
ess
r
u
les.
Sev
er
al
attem
p
ts
h
av
e
b
ee
n
m
ad
e
to
d
e
v
elo
p
r
ea
lis
tic
b
u
g
d
atasets
,
s
u
ch
as
Def
ec
ts
4
J
[
1
4
]
,
Seem
Seed
[
1
5
]
,
an
d
g
u
id
ed
f
au
lt
s
ee
d
in
g
[
9
]
.
Ho
wev
er
,
th
ese
ap
p
r
o
ac
h
es
h
av
e
lim
itatio
n
s
r
elate
d
to
ap
p
licatio
n
d
o
m
ain
co
v
e
r
ag
e,
co
d
e
ar
ch
itectu
r
e
,
o
r
m
u
tatio
n
s
p
er
f
o
r
m
ed
o
n
o
n
ly
a
s
in
g
l
e
lin
e.
I
n
a
d
d
itio
n
,
m
o
s
t
m
eth
o
d
s
d
o
n
o
t
ex
p
licit
ly
co
n
s
id
er
f
u
n
ctio
n
al
s
ce
n
ar
io
s
o
r
u
s
er
p
r
o
ce
s
s
f
lo
ws
as
th
e
b
asis
f
o
r
f
a
u
lt
in
jectio
n
.
B
ased
o
n
th
ese
is
s
u
es,
th
is
s
tu
d
y
in
tr
o
d
u
ce
s
a
r
ea
lis
tic,
g
u
id
ed
,
an
d
co
n
tex
t
u
al
f
au
lt
in
jectio
n
m
o
d
el
f
o
r
s
y
s
tem
f
u
n
ctio
n
ality
in
a
b
lack
-
b
o
x
test
in
g
en
v
ir
o
n
m
en
t
f
o
r
web
ap
p
licatio
n
s
.
T
h
is
s
tu
d
y
h
as
th
r
ee
R
esear
ch
Qu
esti
o
n
s
(
R
Qs):
Ho
w
ca
n
a
f
a
u
lt
in
jectio
n
m
eth
o
d
b
e
d
esig
n
e
d
to
co
n
s
tr
u
ct
r
e
alis
tic
b
u
g
d
atasets
?
(
R
Q1
)
;
"Wh
at
ar
e
th
e
ch
ar
ac
ter
is
tics
o
f
m
u
lti
-
lin
e
b
u
g
s
in
r
ea
l
s
y
s
tem
s
,
an
d
h
o
w
ca
n
th
ey
b
e
r
ep
licated
in
a
co
n
tr
o
lled
m
an
n
er
?"
(
R
Q2
)
;
an
d
"Ho
w
ca
n
th
e
v
alid
atio
n
an
d
r
ep
r
o
d
u
cib
ilit
y
o
f
in
jecte
d
b
u
g
s
en
s
u
r
e
co
m
p
atib
ilit
y
with
th
e
tar
g
eted
test
s
ce
n
ar
io
s
?"
(
R
Q3
)
.
T
o
ad
d
r
ess
th
ese
th
r
ee
R
Qs,
t
h
is
s
tu
d
y
p
r
o
p
o
s
es
a
s
ce
n
ar
io
-
d
r
iv
en
f
a
u
lt
in
jectio
n
(
SDFI)
ap
p
r
o
ac
h
.
SDFI
is
a
f
au
lt
-
s
ee
d
in
g
ap
p
r
o
ac
h
th
at
g
e
n
er
ates
f
au
lts
in
a
tar
g
eted
m
a
n
n
er
an
d
f
o
c
u
s
es
o
n
f
u
n
ctio
n
al
r
eq
u
ir
em
e
n
t
s
ce
n
ar
io
s
r
ath
er
th
an
s
y
n
tax
-
b
ased
r
an
d
o
m
m
u
tatio
n
s
.
T
h
is
ap
p
r
o
ac
h
in
t
eg
r
ates
th
r
ee
m
ain
elem
en
ts
,
n
am
ely
ii
)
we
b
f
a
u
lt
tax
o
n
o
m
y
to
d
eter
m
in
e
e
r
r
o
r
-
p
r
o
n
e
ar
ea
s
,
ii
)
f
au
lt
in
j
ec
tio
n
p
atter
n
s
to
f
o
r
m
u
late
co
d
e
m
o
d
i
f
icatio
n
s
th
at
r
esem
b
le
r
ea
l
b
u
g
s
,
an
d
iii
)
s
ce
n
ar
io
-
b
ased
m
u
tatio
n
test
in
g
is
th
en
u
s
ed
to
s
y
s
tem
atica
lly
in
s
er
t
f
au
lts
.
B
y
co
m
b
in
i
n
g
t
h
ese
elem
en
t
s
,
SDFI
ca
n
p
r
o
d
u
ce
r
ea
lis
tic,
m
u
lti
-
lin
e,
an
d
r
eq
u
ir
em
e
n
ts
-
b
ased
f
au
lts
.
Fau
lt
in
s
er
tio
n
ev
alu
atio
n
u
s
es
ex
p
er
im
en
tal
v
alid
atio
n
o
n
r
ea
l
web
ap
p
licatio
n
s
.
E
ac
h
m
u
tan
t
is
v
alid
ated
with
th
e
r
ea
ch
ab
ilit
y
in
f
ec
tio
n
p
r
o
p
ag
atio
n
(
R
I
P)
m
o
d
el.
T
h
is
e
n
s
u
r
es
th
at
th
e
f
au
lt
is
ex
ec
u
ted
,
th
e
s
y
s
tem
'
s
in
ter
n
al
s
tate
is
in
f
ec
ted
,
an
d
th
e
f
au
lt'
s
im
p
ac
t
is
p
r
o
p
ag
ate
d
to
th
e
p
r
o
g
r
am
o
u
tp
u
t.
E
x
p
er
im
en
tal
v
alid
atio
n
p
r
o
v
e
s
th
e
p
r
ac
ticality
an
d
ef
f
ec
tiv
e
n
ess
o
f
th
e
SDFI
ap
p
r
o
ac
h
in
g
en
er
atin
g
r
ea
lis
tic
b
u
g
d
atasets
.
2.
RE
L
AT
E
D
WO
RK
Fau
lt
in
jectio
n
s
h
av
e
b
ee
n
u
s
ed
to
ev
alu
ate
th
e
e
f
f
ec
tiv
en
e
s
s
o
f
test
in
g
tech
n
iq
u
es.
Ho
w
ev
er
,
m
o
s
t
m
u
tatio
n
test
in
g
-
b
ased
m
et
h
o
d
s
s
till
r
ely
o
n
f
ir
s
t
o
r
d
er
m
u
tan
ts
(
FOM)
,
wh
ich
ar
e
s
im
p
le
s
y
n
tactic
o
p
er
ato
r
s
r
an
d
o
m
l
y
a
p
p
lied
to
t
h
e
e
n
tire
co
d
e
b
ase
u
s
in
g
a
m
u
tatio
n
to
o
l
[
1
2
]
,
[
1
1
]
.
R
an
d
o
m
m
u
tatio
n
s
o
f
ten
p
r
o
d
u
ce
f
au
lts
th
at
ar
e
n
o
t
r
ep
r
esen
tat
iv
e
o
f
r
ea
l
b
u
g
s
.
Sev
e
r
al
s
tu
d
ies
s
h
o
w
th
at
m
u
tatio
n
o
p
er
at
o
r
s
m
o
d
i
f
y
o
n
ly
a
s
in
g
le
ex
p
r
ess
io
n
o
n
a
s
in
g
le
lin
e.
Ho
wev
e
r
,
t
h
ey
f
ail
to
r
ep
licate
th
e
ch
a
r
ac
ter
is
tics
o
f
r
ea
l
b
u
g
s
,
s
u
c
h
as
in
ter
ac
tio
n
b
etwe
en
co
m
p
o
n
e
n
ts
,
m
u
lti
-
lin
e
b
u
g
s
an
d
r
eq
u
i
r
em
en
t
-
b
ased
l
o
g
ic
er
r
o
r
s
.
As
a
r
esu
lt,
th
e
f
au
lts
p
r
o
d
u
ce
r
ed
u
ce
f
au
lt
r
ep
r
esen
tativ
en
ess
v
alu
e,
wh
ich
in
tu
r
n
lead
s
to
d
if
f
ic
u
lt
-
to
-
ev
al
u
ate
m
u
tan
t
ex
p
lo
s
io
n
tr
ig
g
er
s
[
1
2
]
,
[
1
0
]
.
B
u
g
-
r
ep
o
r
t
-
d
r
iv
e
n
Fau
lt
I
n
jec
tio
n
(
iB
iR
)
[
1
6
]
d
em
o
n
s
tr
ates
a
cu
ttin
g
-
ed
g
e
a
p
p
r
o
ac
h
to
g
en
er
atin
g
r
ea
lis
tic
b
u
g
d
atasets
.
i
B
i
R
u
tili
ze
s
I
R
-
b
ased
f
au
lt
lo
ca
lizatio
n
to
id
en
tify
co
d
e
lo
ca
tio
n
s
r
elev
an
t
to
b
u
g
d
escr
ip
tio
n
s
in
b
u
g
r
e
p
o
r
ts
.
Su
b
s
eq
u
en
tly
,
f
au
lt
in
jectio
n
s
u
s
e
r
ev
er
s
ed
f
ix
p
atter
n
s
d
e
r
iv
ed
f
r
o
m
r
ea
l
r
ep
ai
r
p
atter
n
s
.
T
h
is
m
eth
o
d
s
ig
n
if
ican
tly
im
p
r
o
v
es
r
ea
lis
m
b
y
lin
k
in
g
r
ea
l
-
wo
r
ld
b
u
g
s
ce
n
ar
io
s
to
in
jectio
n
lo
ca
tio
n
s
,
as
ev
id
en
ce
d
b
y
a
s
em
an
tic
s
im
ilar
ity
o
f
0
.
5
7
7
an
d
f
au
lt
co
u
p
lin
g
o
f
~3
6
%,
wh
ich
is
m
u
ch
h
ig
h
er
th
an
tr
ad
itio
n
al
m
u
tatio
n
test
in
g
.
Ho
wev
er
,
iB
iR
h
as
lim
i
tatio
n
s
:
it
is
o
n
ly
ap
p
licab
le
to
s
y
s
tem
s
with
a
h
is
to
r
y
o
f
b
u
g
r
ep
o
r
ts
,
m
u
tati
o
n
s
ar
e
lim
ited
to
a
s
in
g
le
lin
e,
an
d
it
d
o
es
n
o
t
y
et
s
u
p
p
o
r
t
r
ep
r
o
d
u
cin
g
f
a
u
lts
b
ased
o
n
r
eq
u
ir
em
en
t
lev
el
b
e
h
av
io
r
th
at
h
as
n
o
t
b
ee
n
r
ec
o
r
d
ed
as
a
b
u
g
.
Fu
r
th
er
m
o
r
e,
f
a
u
lt
in
jectio
n
p
atter
n
d
o
n
o
t
ex
p
licitly
lin
k
f
au
lts
to
u
s
er
p
r
o
ce
s
s
f
lo
ws in
b
u
s
in
ess
ap
p
licatio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
4
,
Au
g
u
s
t
20
2
6
:
2
0
1
4
-
20
30
2016
T
o
ad
d
r
ess
th
e
lim
itatio
n
s
o
f
s
in
g
le
-
lin
e
m
u
tatio
n
,
th
e
co
n
c
ep
t
o
f
h
ig
h
er
o
r
d
er
m
u
tan
ts
(
HOM
)
was
in
tr
o
d
u
ce
d
as
an
ex
ten
s
io
n
o
f
FOM,
wh
er
e
m
u
tan
ts
ar
e
f
o
r
m
ed
th
r
o
u
g
h
th
e
co
m
b
in
ati
o
n
o
f
two
o
r
m
o
r
e
ch
an
g
es
in
th
e
p
r
o
g
r
am
,
allo
win
g
th
em
to
r
e
p
r
esen
t
m
o
r
e
co
m
p
lex
f
a
u
lt
in
ter
ac
tio
n
s
c
o
m
p
ar
ed
to
s
in
g
le
m
u
tatio
n
s
[
1
7
]
.
HOM
in
v
o
lv
es
m
u
lti
-
lin
e
ch
an
g
es
th
at
af
f
ec
t
co
n
tr
o
l
f
lo
w
an
d
co
m
p
o
n
en
t
d
ep
en
d
en
cies,
m
ak
in
g
m
u
tan
ts
m
o
r
e
lik
e
r
ea
l
b
u
g
s
.
I
n
th
e
c
o
n
tex
t
o
f
test
in
g
,
HOM
is
clo
s
ely
r
elate
d
to
t
est
ca
s
es,
wh
er
e
a
s
in
g
le
m
u
tan
t
ca
n
tr
ig
g
er
m
u
l
tip
le
test
in
g
s
ce
n
ar
io
s
,
lead
in
g
to
f
ailu
r
es
ac
r
o
s
s
m
o
r
e
t
h
an
o
n
e
test
ca
s
e.
T
h
is
r
ef
lects
HOM
'
s
ab
ilit
y
to
r
ep
r
esen
t
th
e
p
r
o
p
a
g
atio
n
o
f
f
au
lt
ef
f
ec
ts
with
in
th
e
s
y
s
tem
.
T
h
ese
ch
ar
ac
ter
is
tics
alig
n
with
t
h
e
c
o
u
p
lin
g
e
f
f
ec
t
h
y
p
o
th
esis
,
wh
ich
s
tates
th
at
c
o
m
p
lex
f
au
lts
ar
e
r
elate
d
to
s
im
p
le
f
a
u
lts
,
s
o
th
a
t
FOM
test
ca
s
e
s
h
av
e
th
e
p
o
ten
tial to
d
etec
t H
OM
[
1
8
]
.
HOM
is
clas
s
if
ied
in
to
two
m
ain
d
im
en
s
io
n
s
:
s
u
b
s
u
m
in
g
an
d
co
u
p
lin
g
[
1
7
]
.
T
h
e
s
u
b
s
u
m
in
g
d
im
en
s
io
n
r
ef
er
s
to
th
e
d
if
f
icu
lty
lev
el
o
f
th
e
m
u
tan
t
to
b
e
k
i
lled
,
wh
ile
th
e
co
u
p
lin
g
d
im
e
n
s
io
n
d
escr
ib
es
th
e
r
elatio
n
s
h
ip
b
etwe
en
test
ca
s
e
s
th
at
k
ill
HOM
an
d
FO
M.
T
h
e
co
m
b
in
atio
n
o
f
th
ese
two
d
im
en
s
io
n
s
r
esu
lts
in
s
ix
ca
teg
o
r
ies
o
f
HOM
,
n
am
e
ly
i
)
s
tr
o
n
g
ly
s
u
b
s
u
m
in
g
an
d
co
u
p
led
,
ii
)
wea
k
ly
s
u
b
s
u
m
in
g
an
d
co
u
p
led
,
iii
)
wea
k
ly
s
u
b
s
u
m
in
g
an
d
d
ec
o
u
p
led
,
iv
)
n
o
n
-
s
u
b
s
u
m
i
n
g
a
n
d
d
ec
o
u
p
led
,
v
)
e
q
u
iv
alen
t
m
u
tan
t,
an
d
vi
)
n
o
n
-
s
u
b
s
u
m
in
g
an
d
co
u
p
led
[
1
8
]
.
T
o
o
v
er
c
o
m
e
th
ese
lim
itatio
n
s
,
th
is
s
tu
d
y
p
r
o
p
o
s
es
a
SDF
I
,
wh
ich
is
a
f
au
lt
in
jectio
n
m
ec
h
an
is
m
b
ased
o
n
f
u
n
ctio
n
al
r
eq
u
ir
e
m
en
t
s
ce
n
ar
io
s
an
d
s
y
s
tem
o
p
er
atio
n
al
s
tr
u
ctu
r
es.
Un
lik
e
iB
iR
,
wh
ich
ex
tr
ac
ts
co
n
tex
t
f
r
o
m
h
is
to
r
ical
b
u
g
r
e
p
o
r
ts
,
SDFI
u
tili
ze
s
s
ce
n
ar
io
-
d
r
iv
en
m
ap
p
in
g
th
at
c
o
m
b
in
e
s
test
ca
s
e
an
aly
s
is
,
o
p
er
atio
n
al
f
au
lt
lo
ca
lizatio
n
,
an
d
web
f
a
u
lt
tax
o
n
o
m
y
t
o
d
er
iv
e
tar
g
ete
d
in
jectio
n
s
in
t
o
s
y
s
tem
b
eh
av
io
r
.
T
h
u
s
,
SDFI
p
r
o
v
id
es
a
f
au
lt
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m
ec
h
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is
m
th
at
is
n
o
t
o
n
ly
r
ea
lis
tic
b
u
t
also
alig
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r
eq
u
ir
em
en
ts
,
en
ab
lin
g
th
e
r
ep
r
o
d
u
ctio
n
o
f
b
u
g
d
atasets
th
at
in
clu
d
e
m
u
lti
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lin
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u
g
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o
m
m
o
n
ly
o
cc
u
r
in
clien
t
-
s
er
v
er
web
ap
p
licatio
n
ar
ch
ite
ctu
r
es.
3.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
d
escr
ib
es
th
e
wo
r
k
f
lo
w
f
o
r
im
p
lem
e
n
tin
g
th
e
SDFI
ap
p
r
o
ac
h
as
a
r
ea
lis
tic
f
au
lt
in
jectio
n
m
o
d
el.
T
h
e
SDFI
m
o
d
el
co
n
s
tr
u
cts
r
ea
lis
tic
b
u
g
d
atasets
f
o
r
b
lack
-
b
o
x
test
in
g
in
a
web
-
b
ased
ap
p
licatio
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e
n
v
ir
o
n
m
en
t.
T
h
e
f
au
lt
in
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n
p
r
o
ce
s
s
in
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f
o
llo
ws
t
h
e
in
jectio
n
s
tep
s
in
iB
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,
as
s
h
o
wn
in
Fig
u
r
e
1
.
T
h
e
s
u
cc
ess
o
f
th
is
r
esear
ch
is
m
ea
s
u
r
ed
t
h
r
o
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g
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an
ex
p
e
r
im
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eth
o
d
th
at
ap
p
lies
all
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tag
es
o
f
th
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SDFI
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o
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el
to
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J
T
K
L
ea
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n
ap
p
licatio
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,
a
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ased
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E
ac
h
e
x
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u
tio
n
s
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is
an
aly
ze
d
to
ass
ess
th
e
ac
cu
r
ac
y
o
f
SDFI
in
in
jectin
g
f
au
lts
.
Fig
u
r
e
1
.
W
o
r
k
f
lo
w
o
f
th
e
s
ce
n
ar
io
-
d
r
iv
en
f
au
lt in
jectio
n
m
o
d
el
T
h
e
SDFI
m
o
d
el
ac
ce
p
ts
two
in
p
u
ts
:
th
e
s
o
u
r
ce
co
d
e
o
f
th
e
web
ap
p
licatio
n
in
J
av
a
an
d
test
ca
s
es
th
at
in
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d
e
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g
g
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i
d
elin
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e
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r
o
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am
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r
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r
ed
in
a
g
it
r
ep
o
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ito
r
y
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r
ee
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er
r
o
r
s
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Me
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ile,
test
ca
s
es
ar
e
wr
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atu
r
al
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g
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ag
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er
i
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r
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f
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n
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r
e
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u
ir
em
en
ts
to
test
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e
b
eh
av
io
r
o
f
web
ap
p
licatio
n
s
o
f
twar
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T
est
ca
s
e
ex
ec
u
tio
n
u
s
in
g
a
b
lac
k
-
b
o
x
test
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p
p
r
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y
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am
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r
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p
u
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ata
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id
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ch
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p
licatio
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T
h
e
o
b
jectiv
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o
f
f
au
lt
in
jectio
n
in
SDFI
is
to
m
u
tate
th
e
p
r
o
g
r
am
ag
ain
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t
th
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tar
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et
f
a
u
lt
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t
h
e
test
ca
s
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o
f
th
e
m
ai
n
f
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m
o
s
t
f
r
e
q
u
en
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ac
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ed
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y
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s
er
s
.
T
h
is
aim
s
to
im
p
r
o
v
e
th
e
ef
f
ec
tiv
en
es
s
o
f
test
ca
s
es
f
o
r
cr
itical
er
r
o
r
s
,
as
f
u
n
ctio
n
al
f
ailu
r
es in
k
ey
f
ea
tu
r
es c
an
ca
u
s
e
s
ig
n
if
ican
t d
is
r
u
p
tio
n
to
s
y
s
tem
o
p
er
atio
n
s
[
1
9
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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N:
2088
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8
7
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8
S
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r
r
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w
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test
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(
A
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2017
T
h
e
SDFI
p
r
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tag
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n
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ii
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o
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t
to
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f
au
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,
iii
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m
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an
d
f
au
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f
f
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v
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m
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R
I
P
m
o
d
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an
d
f
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ate
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FDR
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m
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T
h
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ch
ar
ac
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d
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co
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s
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t
en
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p
r
o
g
r
a
m
s
tates
[
2
0
]
,
[
1
6
]
.
Seco
n
d
,
b
u
g
s
m
u
s
t
b
e
r
elev
an
t
to
th
e
co
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e,
co
n
s
id
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g
d
ata
ty
p
es,
v
ar
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le
n
am
es,
co
n
tr
o
l
s
tr
u
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r
es,
d
ata
f
lo
w,
a
n
d
API
s
/lib
r
ar
ies
u
s
ed
[
2
0
]
.
T
h
ir
d
,
b
u
g
s
m
im
ic
lo
g
ical
er
r
o
r
s
in
v
o
l
v
in
g
m
u
ltip
le
lin
es
[
2
1
]
.
Fo
u
r
th
,
f
au
lt in
jectio
n
tar
g
ets "r
ea
s
o
n
ab
le"
p
ar
ts
o
f
th
e
co
d
e
to
m
im
ic
r
ea
l b
u
g
s
[
1
6
]
.
B
y
r
ef
er
r
in
g
to
r
ea
lis
tic
b
u
g
c
r
iter
ia,
f
au
lt
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io
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d
f
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n
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tan
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n
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e
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r
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at
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r
e
lik
e
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l
f
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lts
.
All
s
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o
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th
e
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ar
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r
r
ied
o
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t
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ally
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e
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f
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an
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p
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ased
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s
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k
in
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wh
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th
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m
u
tan
t
p
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r
a
m
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s
o
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tp
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t
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atch
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h
e
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au
lt
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p
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Un
lik
e
co
n
v
en
tio
n
al
m
u
tatio
n
test
in
g
,
wh
ich
s
ep
ar
ates
th
e
p
r
o
ce
s
s
o
f
m
u
tan
t
cr
ea
tio
n
,
g
e
n
er
ally
p
er
f
o
r
m
ed
r
an
d
o
m
ly
b
y
a
to
o
l,
f
r
o
m
th
e
p
r
e
p
ar
atio
n
o
f
test
ca
s
es,
S
DFI
em
p
h
asizes
th
e
co
llab
o
r
atio
n
o
f
th
ese
two
ac
tiv
ities
th
r
o
u
g
h
m
an
u
al
f
au
lt
in
jectio
n
o
r
ien
ted
to
war
d
s
f
u
n
ctio
n
al
s
ce
n
ar
io
s
.
T
h
is
ap
p
r
o
ac
h
alig
n
s
c
o
n
ce
p
tu
al
b
u
g
s
at
th
e
r
eq
u
i
r
em
en
t
lev
el
wit
h
im
p
lem
e
n
tatio
n
al
b
u
g
s
at
th
e
co
d
e
lev
el,
m
ak
in
g
f
au
lt
in
jectio
n
s
m
o
r
e
tar
g
eted
an
d
co
n
tex
tu
ally
r
elev
a
n
t
to
th
e
s
y
s
tem
'
s
f
u
n
ctio
n
ality
.
T
h
e
SDFI
p
r
o
c
ed
u
r
e
is
d
ef
in
ed
b
y
ex
p
licit,
s
tep
-
by
-
s
tep
r
u
les
to
en
s
u
r
e
r
ep
r
o
d
u
cib
ilit
y
,
as
r
ep
r
esen
ted
b
y
alg
o
r
ith
m
1
an
d
7
d
ec
is
io
n
r
u
les.
Alg
o
r
ith
m
1
.
SDFI
ap
p
r
o
ac
h
a
lg
o
r
ith
m
I
n
p
u
t
O
u
t
p
u
t
:
F
u
n
c
t
i
o
n
a
l
sc
e
n
a
r
i
o
s S,
t
e
st
c
a
s
e
s T
,
f
a
u
l
t
i
n
j
e
c
t
i
o
n
p
a
t
t
e
r
n
s P
(
Ta
b
l
e
1
)
:
S
e
t
o
f
m
u
t
a
n
t
s
M
,
R
I
P
e
v
a
l
u
a
t
i
o
n
r
e
s
u
l
t
s,
F
D
R
m
e
t
r
i
c
1:
2:
3:
4:
5:
6:
7:
8:
9:
10:
11:
12:
13:
14:
15:
initialize M =
∅
for
each scenario s in S do
identify execution path p covered by T
determine fault type f based on scenario mapping rule
select mutation pattern m from P corresponding to f
locate code segment c along path p that matches pattern m
if
c is reachable by at least one test case then
apply mutation m to c to generate mutant μ
execute μ using test cases T
evaluate μ using RIP criteria (R, I, P)
add μ to M
end if
end for
compute FDR = (number of killed mutants) / (total mutants in M)
return
M, RIP results, FDR
D
e
c
i
ss
i
o
n
R
u
l
e
:
S
D
F
I
a
p
p
r
o
a
c
h
1.
S
c
e
n
a
r
i
o
M
a
p
p
i
n
g
R
u
l
e
:
F
u
n
c
t
i
o
n
a
l
s
c
e
n
a
r
i
o
m
u
st
b
e
m
a
p
p
e
d
t
o
a
f
a
u
l
t
t
y
p
e
b
a
se
d
o
n
i
t
s
d
o
mi
n
a
n
t
o
p
e
r
a
t
i
o
n
(
l
o
g
i
c
,
d
a
t
a
,
o
r
i
n
t
e
r
f
a
c
e
)
.
2.
P
a
t
t
e
r
n
M
u
t
a
t
i
o
n
S
e
l
e
c
t
i
o
n
R
u
l
e
:
M
u
t
a
t
i
o
n
p
a
t
t
e
r
n
s m
u
s
t
b
e
s
e
l
e
c
t
e
d
f
r
o
m
T
a
b
l
e
1
a
n
d
a
l
i
g
n
e
d
w
i
t
h
t
h
e
i
d
e
n
t
i
f
i
e
d
f
a
u
l
t
t
y
p
e
.
3.
Lo
c
a
t
i
o
n
S
e
l
e
c
t
i
o
n
R
u
l
e
:
A
p
p
l
y
t
h
e
mu
t
a
t
i
o
n
o
n
l
y
t
o
c
o
d
e
se
g
me
n
t
s
t
h
a
t
ma
t
c
h
t
h
e
sce
n
a
r
i
o
a
n
d
s
u
p
p
o
r
t
t
h
e
s
e
l
e
c
t
e
d
p
a
t
t
e
r
n
(
e
.
g
.
,
c
o
n
d
i
t
i
o
n
a
l
st
a
t
e
me
n
t
s f
o
r
l
o
g
i
c
a
l
f
a
u
l
t
s,
o
r
q
u
e
r
y
s
t
a
t
e
me
n
t
s fo
r
d
a
t
a
f
a
u
l
t
s)
.
4.
R
e
a
c
h
a
b
i
l
i
t
y
R
u
l
e
:
Th
e
se
l
e
c
t
e
d
c
o
d
e
l
o
c
a
t
i
o
n
mu
s
t
b
e
c
o
v
e
r
e
d
b
y
a
t
l
e
a
s
t
o
n
e
t
e
s
t
c
a
se
.
5.
S
e
ma
n
t
i
c
C
h
a
n
g
e
R
u
l
e
:
T
h
e
a
p
p
l
i
e
d
mu
t
a
t
i
o
n
m
u
s
t
i
n
t
r
o
d
u
c
e
a
m
e
a
n
i
n
g
f
u
l
sema
n
t
i
c
d
e
v
i
a
t
i
o
n
i
n
p
r
o
g
r
a
m
b
e
h
a
v
i
o
u
r
.
6.
R
I
P
V
a
l
i
d
a
t
i
o
n
R
u
l
e
:
A
mu
t
a
n
t
i
s
c
o
n
si
d
e
r
e
d
e
f
f
e
c
t
i
v
e
o
n
l
y
i
f
i
t
sa
t
i
sf
i
e
s
t
h
e
R
I
P
c
r
i
t
e
r
i
a
.
7.
F
D
R
E
v
a
l
u
a
t
i
o
n
R
u
l
e
:
T
h
e
e
f
f
e
c
t
i
v
e
n
e
ss
o
f
t
h
e
mu
t
a
t
i
o
n
p
r
o
c
e
ss
i
s m
e
a
s
u
r
e
d
u
si
n
g
t
h
e
F
D
R
met
r
i
c
a
c
r
o
ss
a
l
l
g
e
n
e
r
a
t
e
d
mu
t
a
n
t
s
.
3
.
1
.
Det
er
m
ina
t
io
n
o
f
k
e
y
f
e
a
t
ures
Dete
r
m
in
in
g
t
h
e
a
p
p
licatio
n
'
s
m
ain
f
ea
tu
r
es
en
s
u
r
es
t
h
at
f
a
u
lt
in
jectio
n
s
ce
n
ar
i
o
s
ar
e
ca
r
r
i
ed
o
u
t
o
n
cr
itical
ap
p
licatio
n
f
u
n
ctio
n
s
.
B
ec
au
s
e
J
T
K
L
ea
r
n
o
p
er
ates
as
a
d
ig
ital
lear
n
in
g
p
latf
o
r
m
i
n
h
ig
h
e
r
ed
u
ca
tio
n
,
th
e
id
en
tific
atio
n
o
f
k
e
y
f
ea
tu
r
es
co
n
s
id
er
s
in
ter
ac
tio
n
f
o
r
m
s
,
u
s
er
n
ee
d
s
,
an
d
ty
p
ical
u
s
a
g
e
p
atter
n
s
in
an
e
-
lear
n
in
g
en
v
ir
o
n
m
en
t.
T
h
e
an
aly
s
is
o
f
th
e
ap
p
licatio
n
'
s
m
ain
f
ea
tu
r
e
r
eq
u
ir
em
en
ts
u
s
es
th
e
Hig
h
er
-
E
d
u
ca
tio
n
al
E
-
lear
n
in
g
d
esig
n
p
r
in
cip
les
f
r
am
ewo
r
k
.
T
h
is
f
r
am
ewo
r
k
co
n
s
is
ts
o
f
f
o
u
r
m
eta
-
r
eq
u
ir
em
e
n
ts
,
wh
ich
ar
e
d
e
r
iv
ed
f
r
o
m
i
n
to
t
en
d
esig
n
p
r
in
cip
les
(
DP)
to
s
u
p
p
o
r
t
ac
ce
s
s
ib
ilit
y
,
clar
ity
o
f
co
n
ten
t
s
tr
u
ctu
r
e
,
task
in
ter
ac
tiv
ity
,
f
ee
d
b
ac
k
s
u
p
p
o
r
t,
an
d
m
o
tiv
atio
n
al
el
em
en
ts
[
2
2
]
.
T
h
r
o
u
g
h
th
is
a
p
p
r
o
ac
h
,
th
e
f
au
lt
in
jectio
n
p
r
o
ce
s
s
r
ef
lects th
e
c
o
n
tex
t o
f
o
n
lin
e
lea
r
n
in
g
in
a
h
ig
h
er
e
d
u
ca
tio
n
e
n
v
ir
o
n
m
en
t.
T
h
e
r
esu
lts
o
f
th
e
an
aly
s
is
o
f
th
e
m
ain
f
ea
tu
r
es
o
f
th
e
J
T
K
L
ea
r
n
ap
p
licatio
n
in
T
ab
le
2
s
h
o
w
th
e
ess
en
tial
f
ea
tu
r
es
th
at
ar
e
th
e
m
ain
p
o
in
ts
o
f
u
s
er
in
te
r
ac
tio
n
,
n
am
ely
u
s
er
au
t
h
en
ticatio
n
,
co
u
r
s
e
p
a
g
e
ac
ce
s
s
,
m
ater
ial
ac
ce
s
s
,
q
u
iz
co
m
p
le
tio
n
,
q
u
iz
r
esu
lt
o
v
er
v
iew,
a
n
d
lear
n
in
g
p
r
o
g
r
ess
m
o
n
ito
r
in
g
.
T
h
ese
f
ea
tu
r
es
wer
e
ex
tr
ac
ted
f
r
o
m
test
ca
s
es
an
d
s
o
u
r
ce
co
d
e
th
at
h
a
d
th
e
h
ig
h
est
u
s
ag
e
r
ate
a
n
d
wer
e
d
i
r
ec
tly
r
elate
d
to
th
e
p
r
in
cip
les
o
f
DP1
(
Acc
ess
ib
ilit
y
)
,
DP2
(
C
o
n
ten
t
Av
aila
b
ilit
y
)
,
DP3
(
I
n
ter
ac
tiv
e
T
ask
s
)
,
DP4
(
Simp
le
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I
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t J E
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m
p
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n
g
,
Vo
l.
1
6
,
No
.
4
,
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g
u
s
t
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6
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4
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30
2018
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ig
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ctu
r
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,
DP5
(
Feed
b
ac
k
)
,
DP6
(
Mu
lt
im
ed
ia
Su
p
p
o
r
t)
,
an
d
DP8
(
Pro
g
r
ess
T
r
ac
k
in
g
)
.
Me
an
wh
ile,
s
ev
er
al
o
th
er
d
esi
g
n
p
r
in
ci
p
les,
s
u
ch
as
DP7
(
Did
ac
tic
E
lem
en
ts
)
,
DP9
(
Mo
tiv
atio
n
al
E
lem
en
ts
)
,
an
d
DP1
0
(
C
o
m
m
u
n
icatio
n
Me
ch
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is
m
s
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,
wer
e
n
o
t
i
n
clu
d
ed
b
ec
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s
e
t
h
ey
we
r
e
n
o
t
alig
n
ed
with
th
e
ar
ch
itectu
r
e
an
d
f
u
n
ctio
n
al
s
c
o
p
e
o
f
th
e
J
T
K
L
ea
r
n
a
p
p
licatio
n
.
T
ab
le
1
.
SDFI
f
au
lt in
jectio
n
p
atter
n
P
a
t
t
e
r
n
C
o
n
t
e
x
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a
t
e
g
o
r
y
B
u
g
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n
j
e
c
t
i
o
n
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a
t
t
e
r
n
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a
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p
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e
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n
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u
t
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l
e
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u
t
p
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t
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n
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t
a
t
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n
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a
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t
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t
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m
e
n
t
f
i
e
l
d
i
n
q
u
e
r
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e
c
t
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f
i
e
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d
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f
i
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d
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R
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T
f
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,
f
i
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f
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M
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r
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a
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m
e
t
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g
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r
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p
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y
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r
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r
o
u
p
s
f
au
lts
b
y
h
ig
h
-
lev
el
ar
ch
itectu
r
al
ch
a
r
ac
ter
is
tics
o
f
web
ap
p
licatio
n
s
.
Seco
n
d
,
Vijay
ar
ag
h
av
an
an
d
Kan
er
'
s
(
2
0
0
3
)
f
au
lt
class
if
icatio
n
em
p
h
asizes
an
o
m
alo
u
s
u
s
er
in
ter
ac
tio
n
b
eh
a
v
io
r
.
T
h
ir
d
,
I
iv
an
ain
en
'
s
d
ef
ec
t
tax
o
n
o
m
y
(
2
0
2
1
)
ca
teg
o
r
izes
d
ef
e
cts
in
m
o
d
er
n
web
ap
p
licatio
n
s
in
to
n
in
e
ca
teg
o
r
ies.
I
n
ad
d
itio
n
,
th
e
s
elec
tio
n
o
f
f
au
lt
t
y
p
es
co
n
s
id
er
s
alig
n
m
en
t
with
th
e
ap
p
licatio
n
ar
ch
itectu
r
e,
th
e
e
-
lear
n
in
g
d
o
m
ain
,
p
ag
e
s
tr
u
ct
u
r
e,
an
d
u
s
er
in
ter
ac
tio
n
with
th
e
d
ata
r
etr
iev
al
p
r
o
ce
s
s
,
co
n
ten
t
r
ep
r
esen
tati
o
n
,
an
d
b
u
s
in
ess
lo
g
ic
p
r
o
c
ess
in
g
in
th
e
J
T
K
L
ea
r
n
ap
p
licatio
n
.
T
h
u
s
,
th
e
s
elec
ted
f
au
lt
ty
p
es
in
clu
d
e
d
atab
ase
f
au
lts
,
h
y
p
er
lin
k
ed
s
t
r
u
ctu
r
e
f
a
u
lts
,
er
r
o
r
/war
n
in
g
n
o
tific
atio
n
f
a
u
lts
,
m
ed
ia
f
au
lts
,
d
ea
d
-
en
d
/o
r
p
h
a
n
p
ag
e
f
a
u
lts
,
Gr
ap
h
ical
User
I
n
ter
f
ac
e
(
GUI
)
f
a
u
lts
,
an
d
lo
g
ic
an
o
m
alies.
All
f
au
lt
ca
teg
o
r
ies
s
er
v
e
as
th
e
b
asis
f
o
r
d
ev
elo
p
in
g
f
au
lt
in
j
ec
tio
n
s
ce
n
ar
io
s
p
ec
if
icatio
n
s
th
r
o
u
g
h
a
s
ce
n
ar
io
-
d
r
iv
e
n
m
ap
p
in
g
ap
p
r
o
ac
h
,
wh
ich
s
y
s
tem
atica
lly
lin
k
s
J
T
K
L
ea
r
n
u
s
ag
e
s
ce
n
ar
io
s
,
s
y
s
tem
o
p
er
atio
n
al
l
o
ca
tio
n
s
,
an
d
th
e
ty
p
es
o
f
f
au
lts
th
at
m
ay
ar
is
e
alo
n
g
th
e
ex
ec
u
tio
n
p
ath
.
T
h
e
s
ce
n
ar
io
s
a
r
e
d
ev
elo
p
e
d
b
ased
o
n
f
u
n
ctio
n
al
test
ca
s
es,
s
o
th
at
ea
ch
test
s
tep
r
ep
r
esen
tin
g
u
s
er
b
eh
av
io
u
r
s
in
c
r
itical
f
u
n
ctio
n
o
p
er
atio
n
s
is
t
r
an
s
f
o
r
m
ed
in
t
o
a
ca
n
d
id
ate
f
a
u
lt
in
j
ec
tio
n
lo
ca
tio
n
.
T
h
e
d
eter
m
in
atio
n
o
f
f
au
lt
ty
p
es
f
r
o
m
th
e
we
b
f
a
u
lt
tax
o
n
o
m
y
co
n
s
id
er
s
i
n
jectio
n
p
o
in
ts
i
n
a
web
ar
c
h
itectu
r
e,
wh
ich
g
en
er
ally
h
av
e
th
r
ee
tier
s
: c
lien
t
-
s
id
e
(
f
r
o
n
t e
n
d
)
,
s
er
v
er
-
s
id
e
(
b
ac
k
en
d
)
,
a
n
d
d
ata
s
to
r
ag
e
(
d
atab
ase)
.
T
h
e
r
esu
lt
o
f
th
e
o
p
er
atio
n
al
f
au
lt
lo
ca
lizatio
n
p
r
o
ce
s
s
es
co
n
n
ec
ts
f
ailin
g
test
ca
s
es,
t
ar
g
et
f
au
lt
ap
p
licatio
n
s
,
an
d
f
au
lt
ty
p
es.
T
ar
g
et
f
au
lts
d
escr
ib
e
th
e
f
au
l
t
s
p
ec
if
icatio
n
s
in
s
er
ted
s
o
th
a
t
test
ca
s
es
b
ec
o
m
e
f
au
lts
an
d
p
r
o
g
r
am
s
h
av
e
b
u
g
s
.
Fau
lt
ap
p
licatio
n
s
ar
e
th
e
im
p
ac
t
o
f
b
u
g
s
o
n
ap
p
li
ca
tio
n
f
u
n
ctio
n
ality
.
Me
an
wh
ile,
f
au
lt ty
p
es a
r
e
f
au
lt c
ateg
o
r
ies s
elec
ted
f
r
o
m
th
e
web
f
au
lt tax
o
n
o
m
y
.
3
.
3
.
F
a
ult
inje
ct
io
n desi
g
n
T
h
e
f
au
lt
in
jectio
n
d
esig
n
in
th
is
s
tu
d
y
ad
ap
ts
th
e
f
au
lt
in
j
ec
tio
n
p
atter
n
s
p
r
in
ci
p
le
f
r
o
m
th
e
iB
i
R
ap
p
r
o
ac
h
[
1
6
]
,
wh
ich
f
o
r
m
s
i
n
jectio
n
p
atter
n
s
th
r
o
u
g
h
in
v
er
s
io
n
o
f
AST
-
b
ased
f
ix
p
atter
n
s
.
I
n
iB
iR
,
ea
ch
p
atter
n
is
d
ef
in
ed
b
y
an
A
ST
n
o
d
e
co
n
tex
t
(
wh
er
e
th
e
p
atter
n
is
ap
p
lied
)
an
d
a
r
ec
ip
e
(
s
y
n
tactic
m
o
d
if
icatio
n
)
.
Mu
tatio
n
p
o
in
t
s
ar
e
d
is
co
v
e
r
ed
b
y
tr
av
er
s
in
g
th
e
in
jectio
n
lo
ca
tio
n
s
u
b
t
r
ee
th
r
o
u
g
h
s
ea
r
ch
in
g
f
o
r
AST
n
o
d
es th
at
m
atch
th
e
f
au
lt p
atter
n
[
1
6
]
.
T
h
is
s
tu
d
y
a
d
o
p
ts
f
au
lt p
atter
n
s
as th
e
b
asis
f
o
r
p
r
o
g
r
am
co
d
e
ch
an
g
es,
b
u
t
th
e
a
p
p
licatio
n
is
s
elec
tiv
e
b
y
ass
o
ciatin
g
ea
ch
in
jectio
n
p
atter
n
with
f
ailin
g
test
ca
s
es
th
at
v
io
late
f
u
n
ctio
n
al
r
eq
u
ir
em
en
t
s
.
Un
lik
e
iB
iR
,
wh
ich
f
o
cu
s
es
o
n
in
jectio
n
in
to
u
tili
ty
lib
r
ar
y
co
d
e,
th
e
ap
p
licatio
n
o
f
in
jecti
o
n
p
atter
n
s
in
th
e
SDFI
m
o
d
el
is
ex
ten
d
ed
to
co
d
e
elem
en
ts
th
at
af
f
ec
t
th
e
f
u
n
ctio
n
al
b
e
h
av
io
r
o
f
e
-
le
ar
n
in
g
ap
p
licatio
n
s
,
in
clu
d
in
g
HT
ML
f
iles
,
SQL
q
u
er
ies,
en
d
p
o
in
t
ca
lls
,
DOM
m
an
ip
u
latio
n
v
ia
J
av
aScr
ip
t,
a
n
d
p
r
o
ce
s
s
in
g
lo
g
ic
o
n
b
o
th
th
e
clien
t
a
n
d
s
er
v
e
r
s
id
es.
T
h
is
ad
ap
tatio
n
o
v
e
r
co
m
es
th
e
lim
itatio
n
s
o
f
iB
iR
i
n
in
jectin
g
co
n
tex
t
-
b
ased
f
au
lts
,
s
u
ch
as
th
e
in
s
e
r
tio
n
o
f
i
n
ap
p
r
o
p
r
iate
s
tatem
e
n
ts
in
co
n
d
itio
n
b
lo
ck
s
.
T
h
u
s
,
th
e
f
au
lt
in
jectio
n
d
esig
n
in
SDFI
m
ai
n
tain
s
th
e
s
y
n
tactic
p
r
ec
is
io
n
o
f
iB
iR
wh
ile
ad
ju
s
tin
g
p
r
o
d
u
ce
f
u
n
ctio
n
ally
r
elev
an
t
m
u
tatio
n
s
in
th
e
co
n
te
x
t
o
f
web
ap
p
licatio
n
s
.
T
a
b
le
1
s
h
o
ws
th
e
f
au
lt
in
jectio
n
p
atter
n
u
s
ed
in
th
e
SDFI
m
o
d
el.
3
.
4
.
F
a
ult
inje
ct
io
n im
plem
e
nta
t
io
n
Fau
lt
in
jectio
n
is
im
p
lem
en
t
ed
b
y
a
p
p
ly
in
g
s
elec
ted
m
u
tatio
n
p
atter
n
s
to
th
e
id
en
ti
f
ied
co
d
e
lo
ca
tio
n
s
.
E
ac
h
m
u
tan
t
is
co
n
s
tr
u
cted
to
tr
ig
g
er
a
f
ailu
r
e
in
th
e
co
r
r
esp
o
n
d
i
n
g
test
ca
s
e,
t
h
er
eb
y
m
an
i
f
esti
n
g
th
e
in
ten
d
ed
f
au
lt.
No
tab
l
y
,
in
p
r
ac
tice,
a
s
in
g
le
m
u
tan
t
m
ay
af
f
ec
t
m
u
ltip
le
r
elate
d
test
ca
s
es,
wh
ile
a
s
in
g
le
test
ca
s
e
m
ay
also
b
e
ass
o
ciate
d
with
m
u
ltip
le
m
u
ta
n
ts
[
1
2
]
.
3
.
5
.
E
v
a
lua
t
i
o
n
o
f
m
uta
nt
di
s
t
ributio
n
Mu
tan
t
co
r
r
ec
tn
ess
ev
alu
atio
n
ca
n
u
s
e
th
e
R
I
P
m
o
d
el
to
d
e
tect
f
au
lts
th
at
af
f
ec
t
p
r
o
g
r
am
b
eh
av
io
r
th
r
o
u
g
h
test
ca
s
e
ex
ec
u
tio
n
.
D
etec
ted
f
au
lts
in
d
icate
t
h
at
m
u
tan
ts
ar
e
k
illed
.
Me
a
n
wh
ile,
t
h
e
r
atio
o
f
m
u
tan
ts
k
illed
b
y
test
ca
s
es
is
d
ef
in
e
d
as
th
e
m
u
tatio
n
s
co
r
e
[
2
6
]
.
T
h
e
m
u
tatio
n
s
co
r
e
is
ca
lcu
l
ated
u
s
in
g
th
e
f
au
lt
d
etec
tio
n
r
ate
(
FDR
)
,
th
e
p
e
r
c
en
tag
e
o
f
all
m
u
tan
ts
k
illed
.
A
m
u
tan
t
is
co
n
s
id
er
ed
k
illed
if
it
s
ati
s
f
ies
th
e
r
ea
ch
ab
ilit
y
,
in
f
ec
tio
n
,
an
d
p
r
o
p
ag
at
io
n
(
R
I
P)
co
n
d
itio
n
s
[
2
7
]
,
wh
er
e
a
test
ca
s
e
ex
ec
u
tes
th
e
m
u
tated
c
o
d
e,
p
r
o
d
u
ce
s
a
d
ev
iatio
n
in
th
e
p
r
o
g
r
am
’
s
i
n
ter
n
al
s
tate,
an
d
p
r
o
p
ag
ates
th
is
ef
f
e
ct
to
o
b
s
er
v
ab
le
o
u
tp
u
t
b
eh
av
i
o
r
.
On
th
e
o
th
er
h
an
d
,
m
u
tan
ts
r
em
ain
aliv
e
wh
en
Evaluation Warning : The document was created with Spire.PDF for Python.
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2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
4
,
Au
g
u
s
t
20
2
6
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0
1
4
-
20
30
2020
th
ey
f
ail
to
m
ee
t o
n
e
o
f
th
e
R
I
P c
o
n
d
itio
n
s
[
2
7
]
,
n
am
el
y
,
th
e
m
u
tan
t is n
o
t r
ea
ch
ab
le
b
y
th
e
test
ca
s
e,
th
e
co
d
e
is
n
o
t in
f
ec
ted
,
o
r
th
e
m
u
tatio
n
ef
f
ec
t is n
o
t p
r
o
p
a
g
ated
[
2
8
]
.
I
n
th
is
s
tu
d
y
,
m
u
tan
t
p
r
o
p
ag
at
io
n
ev
al
u
atio
n
co
m
b
i
n
es
th
e
R
I
P
m
o
d
el
an
d
FDR
m
etr
ics
to
ass
es
s
th
e
ef
f
ec
tiv
en
ess
o
f
f
au
lt
in
jectio
n
o
n
test
ca
s
es
'
ab
ilit
y
to
d
etec
t
er
r
o
r
b
e
h
av
io
r
.
T
ab
le
3
p
r
ese
n
ts
o
b
s
er
v
atio
n
s
o
f
th
e
f
au
lt
in
jectio
n
f
lo
w
in
m
u
tan
ts
ac
r
o
s
s
th
r
ee
R
I
P
p
ar
a
m
eter
s
,
with
ea
ch
m
u
tan
t
co
n
s
id
er
ed
k
illed
if
it
tr
ig
g
er
s
f
u
n
ctio
n
al
f
ailu
r
e
b
e
h
av
io
r
.
Fu
r
th
e
r
m
o
r
e,
th
e
FDR
m
etr
ic
is
ca
lcu
lated
u
s
in
g
(
1
)
to
o
b
tain
th
e
m
u
tatio
n
s
co
r
e.
A
m
u
tatio
n
s
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iate
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v
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Ev
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o
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t
a
t
u
s
S1
S2
S3
S4
1.
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m
u
t
a
t
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o
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t
h
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t
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n
t
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t
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F
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a
t
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n
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o
.
Tr
u
e
Tr
u
e
F
a
l
se
F
a
l
se
3.
F
a
u
l
t
p
r
o
p
a
g
a
t
i
o
n
t
o
o
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ser
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t
p
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s s
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d
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t
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m
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t
a
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p
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c
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t
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t
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c
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l
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t
h
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t
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f
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t
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e
F
a
l
se
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a
l
se
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a
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o
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c
l
u
s
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u
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t
k
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v
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i
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4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
d
em
o
n
s
tr
ates
th
e
SDFI
m
o
d
el
wo
r
k
f
lo
w
b
y
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je
ctin
g
f
au
lts
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t
o
th
e
b
u
s
in
ess
ap
p
licatio
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J
T
K
L
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s
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tat
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tc
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n
d
th
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ac
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.
1
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E
x
perim
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et
up
T
h
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ex
p
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im
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t
ap
p
lies
SDF
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to
th
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J
T
K
L
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r
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ap
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licatio
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to
ev
alu
ate
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u
tatio
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tc
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m
es.
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e
m
eth
o
d
u
s
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ca
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tu
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p
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im
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n
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r
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eth
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y
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tem
u
n
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er
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tu
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y
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s
tag
es
o
f
th
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SDFI
m
o
d
el
p
r
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ce
s
s
ar
e
ap
p
lied
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o
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tu
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ile,
th
e
ex
p
er
im
en
tal
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esu
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e
a
n
aly
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u
s
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g
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al
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atio
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etr
ics.
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h
e
s
elec
tio
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o
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th
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ject
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tu
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ased
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am
ely
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ailab
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o
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ite,
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u
itab
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o
m
ain
ch
ar
ac
ter
is
tics
with
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u
s
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n
ess
ap
p
licatio
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s
,
s
u
p
p
o
r
t
f
o
r
web
ap
p
licatio
n
ar
ch
itectu
r
e,
an
d
th
e
p
latf
o
r
m
'
s
ab
ilit
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to
o
p
er
ate
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r
o
u
g
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m
m
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n
icatio
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etwe
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th
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r
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n
t
en
d
an
d
b
ac
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ased
o
n
Ap
p
licatio
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Pro
g
r
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m
m
in
g
I
n
ter
f
ac
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API
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.
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n
ad
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itio
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o
m
ain
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eter
m
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p
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iliar
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lear
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r
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ased
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r
iter
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J
T
K
L
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th
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tu
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ject.
A
b
r
ief
p
r
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f
ile
o
f
J
T
K
L
ea
r
n
is
p
r
esen
ted
in
T
ab
le
4
.
4
.
2
.
T
a
rg
e
t
f
a
ult
T
h
e
r
esu
lts
o
f
th
e
o
p
e
r
atio
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al
f
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lt
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ca
lizatio
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aly
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is
o
f
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ap
p
licatio
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s
a
g
ain
s
t
h
ig
h
er
ed
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ca
tio
n
d
esig
n
p
r
in
ci
p
al
v
io
latio
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s
h
a
v
e
id
en
tifie
d
3
4
f
au
lts
,
with
th
e
f
au
lt
d
is
tr
ib
u
tio
n
s
h
o
wn
in
T
ab
le
5
.
T
h
e
tar
g
et
f
au
lts
wer
e
d
eter
m
in
ed
in
th
r
ee
s
tep
s
th
at
en
ab
led
f
au
lt
in
jectio
n
in
to
ea
ch
J
T
K
L
ea
r
n
f
ea
tu
r
e
o
p
e
r
atio
n
,
n
am
ely
:
(
1
)
m
a
p
p
in
g
J
T
K
L
ea
r
n
f
ea
tu
r
es
in
to
o
p
er
atio
n
s
g
r
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u
p
e
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ased
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DP,
r
ef
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as
DP
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eh
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io
r
;
(
2
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aly
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et
f
au
lts
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io
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ay
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e
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j
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ased
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n
w
eb
f
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lt
tax
o
n
o
m
y
,
ap
p
licatio
n
ar
ch
itectu
r
e,
an
d
test
ca
s
es
f
r
o
m
f
ailin
g
test
ca
s
es;
an
d
(
3
)
ev
alu
atin
g
wh
eth
er
th
e
f
a
u
lt
s
p
ec
if
icatio
n
s
ar
e
m
u
tu
ally
e
x
clu
s
iv
e.
T
ab
le
5
s
h
o
ws
th
at
7
1
.
4
3
%
(
2
0
o
u
t
o
f
2
8
)
o
f
th
e
d
esig
n
ed
f
au
lt
tar
g
ets
ca
n
s
p
r
ea
d
t
o
th
e
m
ain
b
eh
av
io
r
o
f
th
e
ap
p
licatio
n
.
I
n
co
m
p
ar
is
o
n
,
2
8
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5
7
% (
8
o
u
t o
f
2
8
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o
f
th
e
b
e
h
av
io
r
s
ar
e
m
ain
t
ain
ed
ac
co
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d
in
g
to
s
p
ec
if
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n
s
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s
e
m
o
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t
o
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th
e
b
eh
a
v
io
r
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ac
t
as
p
r
ec
o
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d
itio
n
s
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th
e
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u
n
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n
al
f
l
o
w.
I
n
jectio
n
s
in
to
p
r
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o
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d
itio
n
b
eh
a
v
io
r
s
ca
n
s
to
p
th
e
ex
ec
u
tio
n
f
lo
w,
p
r
ev
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n
tin
g
f
au
lts
f
r
o
m
p
r
o
p
ag
atin
g
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Fo
r
ex
am
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le,
th
e
lo
g
in
f
u
n
ctio
n
is
n
o
t g
iv
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a
f
au
lt
b
ec
au
s
e
th
e
e
r
r
o
r
ter
m
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at
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e
n
e
x
t
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ec
u
tio
n
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n
ad
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itio
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,
f
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ar
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ig
h
ly
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b
y
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ata
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o
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ig
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ity
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in
d
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g
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n
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ass
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m
e
n
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o
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e
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e
f
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r
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th
e
s
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f
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lt
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r
a
cter
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at
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p
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ig
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lated
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f
ec
t
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p
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if
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ality
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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Su
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izze
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s
o
th
at
th
e
b
u
g
d
ataset
c
o
n
tain
s
m
u
tan
ts
e
v
alu
ated
th
r
o
u
g
h
t
h
e
ex
ec
u
tio
n
p
ath
o
f
th
e
r
el
ated
test
ca
s
e.
T
h
e
ex
p
er
im
en
tal
r
esu
lts
s
h
o
w
th
at
o
n
e
f
au
lt
g
en
er
ally
p
r
o
d
u
ce
s
o
n
e
m
u
tan
t,
b
u
t
m
u
tatio
n
s
at
th
e
s
am
e
co
d
e
p
o
in
t
ca
n
ca
u
s
e
d
if
f
er
e
n
t
f
au
lts
d
e
p
en
d
in
g
o
n
th
e
d
ata
an
d
e
x
e
cu
tio
n
p
ath
o
f
th
e
test
ca
s
e,
n
am
ely
Mu
tan
t
-
5
(
ca
u
s
in
g
Fau
lt
-
4
,
Fau
lt
-
6
,
a
n
d
Fau
lt
-
1
1
)
,
M
u
tan
t
-
9
(
ca
u
s
i
n
g
Fau
lt
-
1
0
a
n
d
Fau
lt
-
1
3
)
,
Mu
tan
t
-
1
2
(
ca
u
s
in
g
Fau
lt
-
1
2
an
d
Fau
lt
-
1
6
)
,
M
u
ta
n
t
-
1
6
(
ca
u
s
in
g
Fau
lt
-
1
9
an
d
Fau
lt
-
2
1
)
,
a
n
d
M
u
tan
t
-
2
8
(
ca
u
s
in
g
Fau
lt
-
3
2
an
d
Fau
lt
-
3
3
)
.
T
h
is
co
n
d
itio
n
ca
u
s
es sev
er
al
test
ca
s
es to
f
ail
wh
en
tr
ig
g
er
e
d
b
y
th
e
s
am
e
o
r
d
if
f
er
en
t m
u
ta
n
ts
.
B
ased
o
n
th
e
ty
p
e
o
f
m
u
tatio
n
p
e
r
f
o
r
m
ed
,
T
ab
le
5
s
h
o
ws
th
at
2
4
m
u
ta
n
ts
(
8
5
.
7
1
%)
f
al
l
in
to
t
h
e
Sin
g
le
Mu
tatio
n
(
FOM)
ca
te
g
o
r
y
,
wh
ile
4
m
u
tan
ts
(
1
4
.
2
9
%)
ar
e
m
u
ltil
in
e
m
u
tatio
n
s
(
HOM
)
.
Gen
er
ate
d
HOM
s
ar
e
class
if
ied
as
wea
k
ly
s
u
b
s
u
m
in
g
an
d
c
o
u
p
led
,
d
e
f
in
ed
b
y
|
T
h
|
<
|
⋃
T
i|
,
wh
er
e
T
is
th
e
s
et
o
f
all
test
ca
s
es,
T
i
k
ills
FOM,
an
d
T
h
k
ills
HOM
.
Ov
er
lap
b
etwe
en
T
h
an
d
T
i
o
cc
u
r
s
in
1
4
.
2
9
%
o
f
m
u
tan
ts
,
in
d
icatin
g
HOM
s
ar
e
g
en
er
all
y
h
a
r
d
er
to
k
ill.
Ho
we
v
er
,
test
ca
s
es
th
at
k
ill
HOM
d
o
n
o
t
alwa
y
s
co
v
e
r
th
o
s
e
th
at
k
ill
all
FOM.
I
n
ad
d
itio
n
,
test
ca
s
e
f
ailu
r
es
ca
n
b
e
ca
u
s
ed
b
y
o
n
e
o
r
m
o
r
e
f
au
lts
.
T
h
is
is
b
ec
au
s
e
f
u
n
cti
o
n
al
b
ased
test
ca
s
es
wil
l
test
th
e
ex
p
ec
ted
r
esu
lt
(
2
)
,
wh
ich
ca
n
ca
ll m
a
n
y
in
ter
n
al
f
u
n
ctio
n
s
d
e
p
en
d
in
g
o
n
th
e
v
alid
atio
n
co
n
d
itio
n
s
a
n
d
t
h
e
in
v
o
lv
em
e
n
t
o
f
ar
ch
itectu
r
al
co
m
p
o
n
e
n
ts
.
Me
an
wh
ile,
s
o
m
e
test
ca
s
es
ca
n
d
etec
t
th
e
s
am
e
f
au
lt
d
u
e
to
lo
g
ic
d
ep
en
d
en
cie
s
an
d
d
ata
f
lo
w
b
etwe
en
f
ea
tu
r
es.
Mu
tan
ts
ar
e
s
p
r
ea
d
ac
r
o
s
s
th
e
f
r
o
n
t
en
d
an
d
b
ac
k
en
d
co
d
e
,
wh
ile
d
atab
a
s
e
f
au
lts
o
cc
u
r
in
d
ata
in
ter
a
ctio
n
s
b
etwe
en
th
e
b
ac
k
en
d
an
d
th
e
d
atab
ase
m
an
ag
em
en
t sy
s
tem
.
T
h
e
d
is
tr
ib
u
tio
n
o
f
in
jectio
n
lo
ca
tio
n
s
a
n
d
f
au
lt
p
atter
n
s
is
s
h
o
wn
in
F
ig
u
r
e
4
.
Fig
u
r
e
4
(
a)
p
r
esen
ts
th
e
d
is
tr
ib
u
tio
n
o
f
f
au
lt
ty
p
es
b
ased
o
n
th
e
we
b
f
a
u
lt
tax
o
n
o
m
y
,
w
h
e
r
e
p
r
o
g
r
a
m
an
o
m
aly
a
n
d
GUI
f
au
lts
d
o
m
in
ate,
in
d
icatin
g
th
at
er
r
o
r
s
m
ain
ly
o
cc
u
r
at
t
h
e
f
u
n
ctio
n
al
lo
g
ic
an
d
u
s
er
in
ter
f
ac
e
lev
els
i
n
r
ea
lis
tic
w
eb
ap
p
licatio
n
s
ce
n
ar
i
o
s
.
Dat
ab
ase
-
r
elate
d
f
a
u
lts
f
u
r
t
h
er
h
i
g
h
lig
h
t
t
h
e
s
tr
o
n
g
r
elatio
n
s
h
ip
b
etwe
en
f
u
n
ctio
n
al
s
ce
n
ar
io
s
an
d
d
ata
p
r
o
ce
s
s
in
g
o
p
e
r
atio
n
s
,
wh
ile
less
f
r
e
q
u
en
t
f
a
u
lts
(
e.
g
.
,
m
ed
ia,
er
r
o
r
h
a
n
d
lin
g
,
an
d
n
av
ig
atio
n
s
tr
u
ctu
r
e
f
au
lts
)
r
ef
lect
th
e
d
iv
er
s
ity
o
f
in
jecte
d
er
r
o
r
s
.
On
th
e
o
th
e
r
h
an
d
,
Fig
u
r
e
4
(
b
)
s
h
o
ws
th
e
d
is
tr
ib
u
tio
n
o
f
f
au
lt
in
jectio
n
p
atter
n
s
,
wh
e
r
e
s
tr
in
g
liter
al
m
o
d
if
icatio
n
s
an
d
r
elatio
n
al
o
p
er
ato
r
ch
a
n
g
es
ar
e
th
e
m
o
s
t
p
r
ev
alen
t,
in
d
icatin
g
th
at
m
an
y
m
u
tatio
n
s
af
f
ec
t
lo
g
ical
co
n
d
itio
n
s
an
d
ex
ec
u
tio
n
f
lo
w.
Oth
er
p
atter
n
s
,
wi
th
lo
wer
f
r
eq
u
e
n
cy
,
r
ep
r
esen
t
v
ar
iatio
n
s
at
th
e
im
p
lem
en
tatio
n
le
v
el,
in
clu
d
in
g
m
eth
o
d
ca
lls
,
c
o
n
d
itio
n
al
s
tr
u
ctu
r
es,
an
d
in
ter
f
ac
e
o
r
d
ata
b
ase
-
r
elate
d
elem
en
ts
.
Ov
er
all,
th
ese
d
is
tr
ib
u
tio
n
s
in
d
icate
th
at
th
e
g
en
er
ated
m
u
tan
ts
co
v
er
d
iv
e
r
s
e
f
au
lt
ty
p
es
an
d
im
p
lem
en
tatio
n
p
atter
n
s
d
er
iv
ed
f
r
o
m
th
e
d
e
f
in
ed
f
u
n
ctio
n
al
s
ce
n
ar
io
s
.
E
ac
h
m
u
tan
t
was
ev
alu
ated
u
s
in
g
th
e
R
I
P
m
o
d
el
as
s
h
o
wn
in
T
ab
le
3
to
ass
es
s
r
ea
ch
ab
ilit
y
,
in
f
ec
tio
n
,
an
d
p
r
o
p
ag
atio
n
.
T
a
b
le
7
s
h
o
ws
th
at
2
6
o
f
2
9
m
u
t
an
ts
wer
e
k
illed
,
wh
ile
th
r
ee
m
u
tan
ts
(
Mu
tan
t
-
1
6
,
Mu
tan
t
-
1
7
,
a
n
d
Mu
tan
t
-
2
1
)
in
th
e
FR
-
0
9
Acc
ess
Qu
izz
es
f
ea
tu
r
e
d
id
n
o
t
p
r
o
p
a
g
ate
,
r
esu
ltin
g
in
f
o
u
r
u
n
d
etec
ted
f
a
u
lts
o
u
t
o
f
3
4
.
T
h
e
FDR
is
an
aly
ze
d
f
r
o
m
t
wo
p
er
s
p
ec
tiv
es.
First,
th
e
f
a
u
lt
-
b
ased
FDR
,
as
d
ef
in
ed
i
n
(
1
)
,
is
8
8
.
2
4
%,
in
d
icatin
g
a
h
i
g
h
d
etec
tio
n
r
ate
f
o
r
tar
g
et
f
au
lts
.
Seco
n
d
,
f
r
o
m
a
m
u
tan
t
-
b
ased
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