Shiva Nejati Mehrdad Sabetzadeh Marsha Chechik Steve

Transcription

Matching and Merging of
Statecharts Specifications
Shiva Nejati Mehrdad Sabetzadeh
Marsha Chechik Steve Easterbrook
University of Toronto
Pamela Zave
AT&T Labs
International Conference on Software
Engineering(ICSE’07)
May 23 2007
Background
➜ Collaborative model-based development
➥ Models built and manipulated by distributed teams
➣ Different versions over time
➣ Different variants across a product family
➣ Different features
➣ Different views
2
Example: AT&T Call Logger Feature
➜ Call Logger: Logs call information and makes it
available to subscribers through a web portal
s0
t0 Call logger - voicemail
t2
t1
setup [zone=target]
Call logger - basic
s1 Initialize Links
s2
setup [zone=source]/
callee = participant
Start
setup [zone=target]/
callee = subscriber
s4
Start
s3
setup [zone=source]
t3
Link
Callee
subscriber?Ack
Link
Participant
Timer
Started
participant?Accept [zone=source] OR
subscriber?Accept [zone=target]
participant?Reject [zone=source] OR
participant?TearDown [zone=source] OR
subscriber?Reject [zone=target] OR
subscriber?TearDown [zone=target]
Timer
Started
participant?TearDown OR
subscriber?TearDown
Log
Success
redirectToVoicemail
[zone=target]
s7
participant?Unavail [zone=source] OR
subscriber?Unavail [zone=target] OR
subscriber?TearDown
t6
s6
t4
callee?Ack
t5
s5
participant?Ack
Pending
Wait
Link
Subscriber
Log
Success
t7
Log
Failure
t8
Log
Voicemail
Log
Failure
3
Challenges
➜ Independently-developed models
➥ may have different vocabularies
➥ often overlap
➣ ... and overlapping concepts may be represented differently
➥ may contradict one another
➥ may be partial
4
Modelling Tasks
➜ Activities in distributed development
➥ integration
➥ consistency checking
➥ interaction analysis
➥ refactoring
➥ commonality and variability analysis
➥ verification and validation
5
Modelling Tasks
➜ Activities in distributed development
➥ integration
➥ consistency checking
➥ interaction analysis
➥ refactoring
➥ commonality and variability analysis
➥ verification and validation
Model management operators aim
to facilitate these tasks
5
Model Management
➜ Model management operators
[Bernstein CIDR’03,
Toronto merge grp GaMMa’06]
➥ Match
➣ finding relationships between models
➥ Merge
➣ combining models with respect to
known relationships between them
➥ Diff
➣ finding differences between models
➥ Slice
➣ producing a projection of a model
based on a given criterion
6
Model Management
➜ Model management operators
[Bernstein CIDR’03,
Toronto merge grp GaMMa’06]
➥ Match
➣ finding relationships between models
➥ Merge
➣ combining models with respect to
known relationships between them
➥ Diff
➣ finding differences between models
➥ Slice
➣ producing a projection of a model
based on a given criterion
6
Framework for Model Merging
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s1
Match
t0
t1
sub
pen
t3
d
S
ls
t7
lf
t8
t2
su
su
lp
t5
ts
t6
(s2 , t1 )
o
Merge
C
d
tear
s7
g
il
(s3 , t3 )
o
kk
p
c
T
s6
su
su
lp
su
par
C
t1
s5
T
s6
(s0 , t0 )
par
su
par
t0
Ac
Wk
Ac
Wk
t4
par
Input Models
zon
(c,s)
c
(l,l)
[ID=v]
s
(s4 , t4 )
[ ID=b]
(w,p)
par
(s5 , t5 )
sub
pen
p
r
t
(t,t)
tear
d
(s6 , t6 )
(s7 , t7 )
(l,l)
d
(f,f)
t8
v
ts
tear
lv
kk
(s3 , t2 )
(s,
s)
set
t6
ls
t7
lf
t8
lv
Merged Model
Models &
Relationships
7
s0
s1
s0
s1
I
s2
I
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s3
se
s3
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su
L
s4
su
s4
par
s5
t3
S
t5
s7
g
t2
t4
par
s1
Match
t0
t1
sub
pen
t3
d
S
ls
t7
lf
t8
t2
su
su
lp
t5
ts
t6
(s2 , t1 )
o
Merge
C
d
tear
s7
g
il
(s3 , t3 )
o
kk
p
c
T
s6
su
su
lp
su
par
C
t1
s5
T
s6
(s0 , t0 )
par
su
par
t0
Ac
Wk
Ac
Wk
t4
par
Input Models
zon
(c,s)
c
(l,l)
[ID=v]
s
(s4 , t4 )
[ ID=b]
(w,p)
par
(s5 , t5 )
sub
pen
p
r
t
(t,t)
tear
d
(s6 , t6 )
(s7 , t7 )
(l,l)
d
(f,f)
t8
v
ts
tear
lv
kk
(s3 , t2 )
(s,
s)
set
t6
ls
t7
lf
t8
lv
Merged Model
Models &
Relationships
Allows to hypothesize alternative ways of
mapping and merging different models
7
s0
s1
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se
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se
s3
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su
L
s4
su
s4
par
s5
t3
S
t5
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g
t2
t4
par
s1
Match
t0
t1
sub
pen
t3
d
S
ls
t7
lf
t8
t2
su
su
lp
t5
ts
t6
(s2 , t1 )
o
Merge
C
d
tear
s7
g
il
(s3 , t3 )
o
kk
p
c
T
s6
su
su
lp
su
par
C
t1
s5
T
s6
(s0 , t0 )
par
su
par
t0
Ac
Wk
Ac
Wk
t4
par
Input Models
zon
(c,s)
c
(l,l)
[ID=v]
s
(s4 , t4 )
[ ID=b]
(w,p)
par
(s5 , t5 )
sub
pen
p
r
t
(t,t)
tear
d
(s6 , t6 )
(s7 , t7 )
(l,l)
d
(f,f)
t8
v
ts
tear
t6
lv
kk
(s3 , t2 )
(s,
s)
set
ls
t7
lf
t8
lv
Merged Model
Models &
Relationships
- Heuristic
- Semi-automated
7
s0
s1
s0
s1
I
s2
I
s2
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S
se
s3
se
s3
L
su
L
s4
su
s4
par
s5
t3
S
t5
s7
g
t2
t4
par
s1
Match
t0
t1
sub
pen
t3
d
S
ls
t7
lf
t8
t2
su
su
lp
t5
ts
t6
(s2 , t1 )
o
Merge
C
d
tear
s7
g
il
(s3 , t3 )
o
kk
p
c
T
s6
su
su
lp
su
par
C
t1
s5
T
s6
(s0 , t0 )
par
su
par
t0
Ac
Wk
Ac
Wk
t4
par
Input Models
zon
(c,s)
c
(l,l)
[ID=v]
s
(s4 , t4 )
[ ID=b]
(w,p)
par
(s5 , t5 )
sub
pen
p
r
t
(t,t)
tear
d
(s6 , t6 )
(s7 , t7 )
(l,l)
d
(f,f)
t8
v
ts
tear
t6
lv
kk
(s3 , t2 )
(s,
s)
set
ls
t7
lf
t8
Merged Model
lv
Models &
Relationships
- Heuristic
- Semi-automated
- Exact
- Automated
7
AT&T Call Logger Feature
➜ Call Logger: Logs call information and makes it
available to subscribers through a web portal
s0
t2
t1
setup [zone=target]
Call logger - basic
s1 Initialize Links
s2
Start
callee = subscriber
s4
Start
s3
setup [zone=source]
t3
Link
Callee
subscriber?Ack
Link
Participant
Timer
Started
Timer
Started
subscriber?TearDown
Log
Success
redirectToVoicemail
[zone=target]
s7
subscriber?TearDown
t6
s6
t4
callee?Ack
t5
s5
participant?Ack
Pending
Wait
Link
Subscriber
Log
Success
t7
Log
Failure
t8
Log
Voicemail
Log
Failure
8
Our Goal
➜ Construct specific instantiations of Match and
Merge operators
➥ ... for finding overlaps between Statecharts and
combining them
➥ Issues:
➣ identify model relationships
➣ construct a merge
➣ distinguish between shared and non-shared
parts of the input models
➣ ensure that our operators are correct
9
Our Goal
➜ Construct specific instantiations of Match and
Merge operators
➥ ... for finding overlaps between Statecharts and
combining them
➥ Issues:
➣ identify model relationships
➣ construct a merge
➣ distinguish between shared and non-shared
parts of the input models
➣ ensure that our operators are correct
➥ Challenge
➣ Statecharts combine structural and behavioural information
9
Merge
Match
Related Work
Structural
Behavioural
DB: Schema matching
Formal methods:
Quantitative notions of
behavioural bisimulation
Rahm&Bernstein VLDBJ01
Melnik et al. ICDE02
SE: Support for reuse
Maiden&Sutcliffe CASM92
Ryan&Mathews RE93
Mandelin et al. ICSE06
DB: Schema merging
Melnik et al. SIGMOD02
SE: Graph-based models
Alanen&Porres UML03
Mehra et al. ASE05
Sabetzadeh&Easterbrook REJ06
de Alfaro et al. ICALP04
Sokolsky et al. TACAS06
SE: State-machines and
scenarios
Whittle&Schumann ICSE00
Uchitel&Chechik FSE04
Nejati&Chechik ASE05
10
Our Contributions
➜ A model merging framework for Statecharts
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s2
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se
s3
se
s3
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su
L
s4
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s4
par
s5
s6
t3
S
t5
t2
su
su
lp
s7
g
t4
par
o
Match
t0
t1
sub
pen
t3
d
S
ls
t7
lf
t8
t2
su
su
lp
t5
ts
t6
(s2 , t1 )
o
Merge
C
d
tear
s7
g
il
(s3 , t3 )
kk
p
c
s1
par
C
t1
su
T
T
s6
(s0 , t0 )
par
s5
su
par
t0
Ac
Wk
Ac
Wk
t4
par
Input Models
zon
(c,s)
c
(l,l)
[ID=v]
s
(s4 , t4 )
[ ID=b]
(w,p)
par
(s5 , t5 )
sub
pen
p
r
t
(t,t)
tear
d
(s6 , t6 )
(s7 , t7 )
(l,l)
d
(f,f)
t8
v
ts
tear
lv
kk
(s3 , t2 )
(s,
s)
set
t6
ls
t7
lf
t8
lv
Merged Model
Models &
Relationships
11
Our Contributions
s0
s1
s0
s1
I
s2
I
s2
S
S
se
s3
se
s3
L
su
L
s4
su
s4
par
s5
s6
t3
S
t5
t2
su
su
lp
s7
g
t4
par
o
Match
t0
t1
sub
pen
t3
d
S
ls
t7
lf
t8
t2
su
su
lp
t5
ts
t6
(s2 , t1 )
o
Merge
C
d
tear
s7
g
il
(s3 , t3 )
kk
p
c
s1
par
C
t1
su
T
T
s6
(s0 , t0 )
par
s5
su
par
t0
Ac
Wk
Ac
Wk
t4
par
Input Models
zon
(c,s)
c
(l,l)
[ID=v]
s
(s4 , t4 )
[ ID=b]
(w,p)
par
(s5 , t5 )
sub
pen
p
r
t
(t,t)
tear
d
(s6 , t6 )
(s7 , t7 )
(l,l)
d
(f,f)
t8
v
ts
tear
lv
kk
(s3 , t2 )
(s,
s)
set
t6
ls
t7
lf
t8
lv
Merged Model
Models &
Relationships
-Uses two kinds of heuristics
-Syntactic (static)
-Semantic (behavioural)
11
Match Operator: Syntactic Heuristics
➜ Similarities between state names
➥ typographic similarities:
N-gram matcher
➥ linguistic similarities: WordNet similarity matcher
➜ Similarities between models hierarchy trees
➥ states at similar depths are more likely to be similar
VoiceMail:
Basic:
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TearDown
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participant?Avail
subscriber?Avail
Accept
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TearDown
Unavail
/log VoiceMail
Success
Failure
Success
Failure
VoiceMail
12
N-gram matcher
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Basic:
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subscriber?Avail
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1.0
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Failure
VoiceMail
✔
12
N-gram matcher
0.12
Basic:
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TearDown
✘
VoiceMail:
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subscriber?Avail
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1.0
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Failure
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✔
12
Match Operator: Behavioural Heuristics
➜ Similarities between branching behaviours of
states
➥ Behavioural bisimulation is too strong
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states
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states
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Bisimilar
13
states
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subscriber?Avail
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Bisimilar
13
states
VoiceMail:
Basic:
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TearDown
Pending
participant?Avail
subscriber?Avail
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Bisimilar
13
states
Not Bisimilar ✘
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Bisimilar
13
!
a ! max b
X = s→s
L(a, b) × B (s , t ). And the
!
t→t
Heuristics
arityMatch
degrees Operator:
between the Behavioural
forward neighbours
of
d their
matchesnotion
among
the forward
neighquantitative
of bisimulation
[Sokolsky
et al.
➜ Abest
TACAS06]
of
s
after
iteration
i
−
1
are
computed
by
!
i−1
"
"
Iteratively
aggregate
similarity
degrees
between
a ! ➥
max
b)
×
B
(s
,
t
).
We
denote
b ! L(a,
t→t
i−1
"
"
s→s
forward/backward
i neighbours of a pair of states
um of X and Y by Sum (s, t).
i all states are similar ( B 0 (s, t) = 1 )
Initially
➜
e value of B (s, t) is computed by first normalizing
i−1
i
(s, t)➜and
thent)taking
its average with B (s, t):
is
B (s,
i
B (s, t) =
`
1
2
i
Sum (s,t)
|succ(s)|+|succ(t)|
´
i−1
+ B (s, t)
algorithm
stopsand
when
it converges
or numafter a
➜ The
e above
formula,
|succ(s)|
|succ(t)|
are the
maximum
number
of
iterations
f forward neighbours of s and t, respectively. The
i
r the B (s, t), the more the behaviours of s and t are
bi
w
H
(m
ity
w
us
4.
M
la
de
ar
to
14
Behavioural Matching: Example
VoiceMail:
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subscriber?Avail
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Success
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VoiceMail
15
Behavioural Matching: Example
0.65
Basic:
Wait
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TearDown
✔
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participant?Avail
subscriber?Avail
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Success
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VoiceMail
15
Hybrid Match Operator
➜ Combine syntactic and behavioural similarity
measures
➥ e.g., by taking their linear average
16
measures
➜ Example:
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16
measures
➜ Example:
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➜ The resulting relationship
{(Wait, Pending), (Success, Success), (Failure, Failure)}
➜ This may need user revision
16
Our Contributions
s0
s1
s0
s1
I
s2
I
s2
S
S
se
s3
se
s3
L
su
L
s4
su
s4
par
s5
s6
t3
S
t5
t2
su
su
lp
s7
g
t4
par
o
Match
t0
t1
sub
pen
t3
d
S
ls
t7
lf
t8
t2
su
su
lp
t5
ts
t6
(s2 , t1 )
o
Merge
C
d
tear
s7
g
il
(s3 , t3 )
kk
p
c
s1
par
C
t1
su
T
T
s6
(s0 , t0 )
par
s5
su
par
t0
Ac
Wk
Ac
Wk
t4
par
Input Models
zon
(c,s)
c
(l,l)
[ID=v]
s
(s4 , t4 )
[ ID=b]
(w,p)
par
(s5 , t5 )
sub
pen
p
r
t
(t,t)
tear
d
(s6 , t6 )
(s7 , t7 )
(l,l)
d
(f,f)
t8
v
ts
tear
lv
kk
(s3 , t2 )
(s,
s)
set
t6
ls
t7
lf
t8
lv
Merged Model
Models &
Relationships
17
Our Contributions
s0
s1
s0
s1
I
s2
I
s2
S
S
se
s3
se
s3
L
su
L
s4
su
s4
par
s5
s6
t3
S
t5
t2
su
su
lp
s7
g
t4
par
o
Match
t0
t1
sub
pen
t3
d
S
ls
t7
lf
t8
t2
su
su
lp
t5
ts
t6
(s2 , t1 )
o
Merge
C
d
tear
s7
g
il
(s3 , t3 )
kk
p
c
s1
par
C
t1
su
T
T
s6
(s0 , t0 )
par
s5
su
par
t0
Ac
Wk
Ac
Wk
t4
par
Input Models
zon
(c,s)
c
(l,l)
[ID=v]
s
(s4 , t4 )
[ ID=b]
(w,p)
par
(s5 , t5 )
sub
pen
p
r
t
(t,t)
tear
d
(s6 , t6 )
(s7 , t7 )
(l,l)
d
(f,f)
t8
v
ts
tear
lv
kk
(s3 , t2 )
(s,
s)
set
t6
ls
t7
lf
t8
Merged Model
lv
Models &
Relationships
-Preserves
behavioural and
hierarchical information
-Treats behavioural
discrepancies as variabilities
17
Merging: Example
VoiceMail:
Basic:
RedirectToVM
Wait
Accept
Reject,
TearDown
Pending
participant?Avail
subscriber?Avail
Accept
Reject,
TearDown
Unavail
/log VoiceMail
Success
Failure
Success
Failure
VoiceMail
One Relationship:
{(Wait, Pending),
(Success, Success),
(Failure, Failure)}
18
Merging: Example
VoiceMail:
Basic:
RedirectToVM
Wait
Accept
Reject,
TearDown
Pending
participant?Avail
subscriber?Avail
Accept
Reject,
TearDown
Unavail
/log VoiceMail
Success
Success
Failure
Failure
VoiceMail
One Relationship:
{(Wait, Pending),
(Success, Success),
(Failure, Failure)}
Wait,
participant?Avail
Pending
subscriber?Avail
Accept
Success,
Success
Unavail
[Id = voicemail]
RedirectToVM
[Id = voicemail]
Reject,
TearDown
/log VoiceMail
Failure,
Failure
VoiceMail
18
Merging: Example
VoiceMail:
Basic:
RedirectToVM
Wait
Accept
Reject,
TearDown
Pending
participant?Avail
subscriber?Avail
Accept
Reject,
TearDown
Unavail
/log VoiceMail
Success
Success
Failure
Failure
VoiceMail
After a connection is set up, a successful
call will be logged if the subscriber or
participant sends Accept
Wait,
participant?Avail
Pending
subscriber?Avail
Accept
Success,
Success
Unavail
[Id = voicemail]
RedirectToVM
[Id = voicemail]
Reject,
TearDown
/log VoiceMail
Failure,
Failure
VoiceMail
18
Merging: Example
VoiceMail:
Basic:
RedirectToVM
Wait
Accept
Reject,
TearDown
Pending
participant?Avail
subscriber?Avail
Accept
Reject,
TearDown
Unavail
/log VoiceMail
Success
Success
Failure
Failure
VoiceMail
Wait,
participant?Avail
Pending
subscriber?Avail
Accept
Success,
Success
Unavail
[Id = voicemail]
RedirectToVM
[Id = voicemail]
Reject,
TearDown
/log VoiceMail
Failure,
Failure
VoiceMail
18
Merging: Example
VoiceMail:
Basic:
RedirectToVM
Wait
Accept
Reject,
TearDown
Pending
participant?Avail
subscriber?Avail
Accept
Reject,
TearDown
Unavail
/log VoiceMail
Success
Success
Failure
Failure
VoiceMail
Wait,
participant?Avail
Pending
subscriber?Avail
Accept
Success,
Success
Unavail
[Id = voicemail]
RedirectToVM
[Id = voicemail]
Reject,
TearDown
/log VoiceMail
Failure,
Failure
VoiceMail
18
Merging: Example
VoiceMail:
Basic:
RedirectToVM
Wait
Accept
Reject,
TearDown
Pending
participant?Avail
subscriber?Avail
Accept
Reject,
TearDown
Unavail
/log VoiceMail
Success
Success
Failure
Failure
VoiceMail
After a connection is set up, a voicemail will
be logged if the call is redirected to a
voicemail service
Wait,
participant?Avail
Pending
subscriber?Avail
Accept
Success,
Success
Unavail
[Id = voicemail]
RedirectToVM
[Id = voicemail]
Reject,
TearDown
/log VoiceMail
Failure,
Failure
VoiceMail
18
Merging: Example
VoiceMail:
Basic:
RedirectToVM
Wait
Accept
Reject,
TearDown
Pending
participant?Avail
subscriber?Avail
Accept
Reject,
TearDown
Unavail
/log VoiceMail
Success
Success
Failure
Failure
VoiceMail
voicemail service
Wait,
participant?Avail
Pending
subscriber?Avail
Accept
Success,
Success
Unavail
[Id = voicemail]
RedirectToVM
[Id = voicemail]
Reject,
TearDown
/log VoiceMail
Failure,
Failure
VoiceMail
18
Merging: Example
VoiceMail:
Basic:
RedirectToVM
Wait
Accept
Reject,
TearDown
Pending
participant?Avail
subscriber?Avail
Accept
Reject,
TearDown
Unavail
/log VoiceMail
Success
Success
Failure
Failure
VoiceMail
voicemail service
Wait,
participant?Avail
Pending
subscriber?Avail
Accept
Success,
Success
Unavail
[Id = voicemail]
RedirectToVM
[Id = voicemail]
Reject,
TearDown
/log VoiceMail
Failure,
Failure
VoiceMail
18
Merging: Example
VoiceMail:
Basic:
RedirectToVM
Wait
Accept
Reject,
TearDown
Pending
participant?Avail
subscriber?Avail
Accept
Reject,
TearDown
Unavail
/log VoiceMail
Success
Success
Failure
Failure
VoiceMail
Merge is always sound, but a poor match will yield
a merge with unnecessary parameterization
Wait,
participant?Avail
Pending
subscriber?Avail
Accept
Success,
Success
Unavail
[Id = voicemail]
RedirectToVM
[Id = voicemail]
Reject,
TearDown
/log VoiceMail
Failure,
Failure
VoiceMail
18
Merging: Example
VoiceMail:
Basic:
RedirectToVM
Wait
Accept
Reject,
TearDown
Pending
participant?Avail
subscriber?Avail
Accept
Reject,
TearDown
Unavail
/log VoiceMail
Success
Failure
Success
Failure
VoiceMail
Merge is always sound, but a poor match will yield
a merge with unnecessary parameterization
Accept
[Id = basic]
RedirectToVM
[Id = voicemail]
Reject,
TearDown
Wait,
participant?Avail
Pending
subscriber?Avail
Unavail
[Id = voicemail]
Success
Accept
Failure,
[Id =voicemail]
Failure
Success
/log VoiceMail
VoiceMail
18
Call Logger Feature
s0
t2
t1
setup [zone=target]
Call logger - basic
s1 Initialize Links
s2
Start
callee = subscriber
s4
Start
s3
setup [zone=source]
t3
Link
Callee
subscriber?Ack
Link
Participant
Timer
Started
Timer
Started
subscriber?TearDown
Log
Success
redirectToVoicemail
[zone=target]
s7
subscriber?TearDown
t6
s6
t4
callee?Ack
t5
s5
participant?Ack
Pending
Wait
Link
Subscriber
Log
Success
t7
Log
Failure
t8
Log
Voicemail
Log
Failure
19
A Possible Merge
(s0 , t0 )
Call logger - (basic, voicemail)
s1 Initialize Links
(s2 , t1 ) (Start,
setup [zone =target]/
callee = subscriber
setup [zone=source] /
callee=participant
Start)
(s3 , t3 )
(Link Callee,
Link Participant)
participant?Ack
[ID=voicemail]
(s3 , t2 )
(Link Callee,
Link Subscriber)
callee?Ack
[ID=basic]
subscriber?Ack
[ID=voicemail]
(s4 , t4 )
callee?Ack
[ ID=basic]
(Waiting,
Pending)
redirectToVoicemail
[zone=target,
ID=voicemail]
participant?Unavail [zone=source, ID=voicemail]OR
(s5 , t5 ) (Timer Started,
Timer Started)
subscriber?Unavail [in target-zone, ID=voicemail] OR
subscriber?TearDown
(s6 , t6 )
(Log Success,
Log Success)
(s7 , t7 )
(Log Failure,
Log Failure)
t8
Log
Voicemail
20
Evaluation
➜ Ultimate evaluation:
➥ improves productivity and quality in model-based
development
➜ Our current evaluation
➥ Merge operator (analytically)
➣ soundness
➠ preserves branching behaviours, i.e., traces and trees
➣ scalability -- low complexity
➥ Match operator (empirically)
➣ compare its results with the assessment of a domain expert
21
Evaluation: Match
➠ high precision
➣ few false negatives
➠ high recall
➥ Results
➣ syntactic-- high precision
➠ states with similar names
were likely to correspond
➣ behavioural-- high recall
➣ combined matching
➠ precision higher than the
individual matchings
➠ recall is reasonably high
Precision
➣ few false positives
Call Logger
100%
90%
Combined
80%
70%
Behavioural
60%
Static
50%
40%
30%
20%
10%
0%
0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5
Threshold
100%
90%
80%
70%
60%
50%
40%
30%
0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5
Recall
➥ Desirable match
➥ More evaluation results in the paper
Threshold
22
Complexity
➜ Time Complexity
➥ Match and Merge
➣ O(|trans. of M| × |trans of M’|)
➜ Space Complexity
➥ Match
➣ O(|states of M| × |states of M’|
|trans. of M|
×
+
|trans. of M’|)
➥ Merge
➣ linear in the size of the input models and the correspondence
relation
➠ ... which is max(|states of M|, |states of M’|)
23
Limitations
➜ Match
➥ Need more extensive user trials for better evaluation
➣ We did not evaluate iterative use:
➠ match, merge, refine the match, merge again, etc.
➣ We assumed there is a single “correct” match
➜ Merge
➥ We do not support a number of advanced Statecharts
features
➣ history states, parallel states and transitions, etc
24
Conclusion
➜ Match operator for Statecharts
➥ heuristic
➥ uses structure and behaviour
➜ Merge operator for Statecharts
➥ automatic
➥ preserves structure and behaviour
➜ Proof-of-concept implementations
➥ Merge: www.cs.toronto.edu/~mehrdad/tremer/
➥ Match: www.cs.toronto.edu/~shiva/MatchTool/
25
Thank You!
Questions?

Shiva Nejati Mehrdad Sabetzadeh Marsha Chechik Steve

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