Semantic Model Differencing Utilizing Behavioral Semantics Specifications (Talk at MODELS 2014)

Semantic Model Differencing
Utilizing Behavioral Semantics Specifications
www.modelexecution.org
p Philip g , Langer, j Tanja y Mayerhofer, , Gerti pp
Kappel
Business Informatics Group
Institute of Software Technology and Interactive Systems
Vienna University of Technology
Favoritenstraße 9‐11/1883, 1040 Vienna, Austria
phone: 43 (1) 5880118804 (secretary), fax: 43 (1) 5880118896
office@big.tuwien.ac.at, www.big.tuwien.ac.at

Motivation
Models are subject to change  Change management for models is required
Model differencing
 Goal: Identify differences among models
 Applications: Merging, versioning, conflict detection, incremental testing, etc.
Syntactic model differencing
 Procedure: Matching ‐ Differencing
 Output: Syntactic differences (add, delete, update operations)
Semantic model differencing
 Takes semantics of models into account
 Enables additional analyses of changes (e.g., semantic preservation)
 Provides basis for comprehending the evolution of models
2

Motivating Example Syntactic Model Differencing
M1
p3
p1 t1 p2 t2 p4
M2
+
p1 t1 p2 t2 p3 t3 p
p4
3

M1 1. Matching
 Identify corresponding elements
based on
 Identifiers
p3
 Signatures
 Similarity
p1 t1 p2 t2 p4
 Custom matching
M2
+
p1 t1 p2 t2 p3 t3 p
p4
4

M1 1. Matching
based on
 Identifiers
p3
 Signatures
 Similarity
p1 t1 p2 t2 p4
 Custom matching (names)
M2
+
p1 t1 p2 t2 p3 t3 p
p4
5

M1 1. Matching
based on
 Identifiers
p3
 Signatures
 Similarity
p1 t1 p2 t2 p4
2. Differencing
M2
 Compare corresponding
elements
+
p1 t1 p2 t2 p3 t3 p4
 Identify non‐corresponding
elements
p
6

M1 1. Matching
based on
 Identifiers
p3
 Signatures
 Similarity
p1 t1 p2 t2 p4
p3 removed output
p3 removed as input
p3 added output
2. Differencing
M2
as as !
p4 removed as output
!
elements
+
+
p1 t1 p2 t2 p3 t3 p4
elements
p
t3 added
7

Which impact do the changes have on the model( model(‘s semantics)?
M1 1. Matching
based on
 Identifiers
p3
 Signatures
 Similarity
p1 t1 p2 t2 p4
p3 removed output
p3 removed as input
p3 added output
2. Differencing
M2
as as !
p4 removed as output
!
elements
+
+
p1 t1 p2 t2 p3 t3 p4
elements
p
t3 added
8

Introduction to Semantic Model Differencing
Semantics of a modeling language1
Mapping : Language → Semantic domain
 Assigns meaning to each conformant model
 Provides (semantic) interpretations of models
Semantic model differencing
 Procedure:
 Obtain semantic interpretations of models
 Analyze semantic interpretations
 Output: Semantic differences
 Semantic interpretations valid for one model but not for the other one
 Witness semantic differences among models (diff witnesses2)
9
1 D. Harel, B. Rumpe. Meaningful Modeling: What’s the Semantics of “Semantics”? IEEE Computer, 37(10):64–72, 2004.
2 S. Maoz et al. A Manifesto for Semantic Model Differencing. MODELS’10, volume 6627 of LNCS, pages 194–203.
Springer, 2011.

Motivating Example Semantic Model Differencing
Petri nets
Semantics
 Firing of transitions leading to markings
(token distributions)
M1
Semantic p3
interpretation
 Traces (order of transition firings)
 Reachable markings
p1 t1 p2 t2 p4
Semantic difference
l l d d l
M2
 Traces only valid in one model
 Markings only reachable in one model
+
p1 t1 p2 t2 p3 t3 p4  Final p markings only reached by one model
10

Petri nets
Semantics
 Firing of transitions leading to markings
(token distributions)
M1
Semantic p3
interpretation
 Traces (order of transition firings)
 Reachable markings
p1 t1 p2 t2 p4
Semantic difference
l l d d l
M2
 Traces only valid in one model
 Markings only reachable in one model
+
p1 t1 p2 t2 p3 t3 p4  Final p markings only reached by one model
11

Petri nets
Semantic interpretation
M1
M : p1=1 0 0 p3
M0,M1: 1, p2=0, p3=0, p4=0
M0
p1 t1 p2 t2 p4
M2
+
p1 t1 p2 t2 p3 t3 p
p4
12

Petri nets
M1
M : p1=1 0 0 p3
M0,M1: 1, p2=0, p3=0, p4=0
M1,M1: p1=0, p2=1, p3=1, p4=0
M0
p1 t1 p2 t2 p4
M2
+
p1 t1 p2 t2 p3 t3 p
p4
13

Petri nets
M1
M : p1=1 0 0 p3
M0,M1: 1, p2=0, p3=0, p4=0
M1,M1: p1=0, p2=1, p3=1, p4=0
M0 M2,M1: p1=0, p2=0, p3=0, p4=1
p1 t1 p2 t2 p4
M2
+
p1 t1 p2 t2 p3 t3 p
p4
14

Petri nets
M1
M : p1=1 0 0 p3
M0,M1: 1, p2=0, p3=0, p4=0
M1,M1: p1=0, p2=1, p3=1, p4=0
M0 M2,M1: p1=0, p2=0, p3=0, p4=1
p1 t1 p2 t2 p4
M2 M0,M2: p1=1, p2=0, p3=0, p4=0
M : p1=0 1 0 +
p1 t1 p2 t2 p3 t3 p4
M1,M2: 0, p2=1, p3=0, p4=0
M2,M2: p1=0, p2=0, p3=1, p4=0
M3,M2: p1=0, p2=0, p3=0, p4=1
M0
p
15

Petri nets
Semantic difference (final marking)
M1
M : p1=1 0 0 p3
M0,M1: 1, p2=0, p3=0, p4=0
M1,M1: p1=0, p2=1, p3=1, p4=0
M0 M2,M1: p1=0, p2=0, p3=0, p4=1
p1 t1 p2 t2 p4
semantically
equivalent
M2 M0,M2: p1=1, p2=0, p3=0, p4=0
M : p1=0 1 0 q
+
p1 t1 p2 t2 p3 t3 p4
M1,M2: 0, p2=1, p3=0, p4=0
M2,M2: p1=0, p2=0, p3=1, p4=0
M3,M2: p1=0, p2=0, p3=0, p4=1
M0
p
16

Petri nets
Semantic difference (final marking)
M1
M : p1=0 1 0 p3
M0,M1: 0, p2=1, p3=0, p4=0
M0
p1 t1 p2 t2 p4 diff witness
M2 M0,M2: p1=0, p2=1, p3=0, p4=0
M : p1=0 0 1 +
p1 t1 p2 t2 p3 t3 p4
M1,M2: 0, p2=0, p3=1, p4=0
M0 M2,M2: p1=0, p2=0, p3=0, p4=1
p
17

Generic Semantic Model Differencing: Motivation
Language‐specific approach (Maoz et al.1 and Fahrenberg et al.2)
 Procedure:
 Translate models into semantic domain
 Perform semantic differencing in semantic domain
 Translate result into modeling language
 Challenge: Complex translations and differencing algorithms
Generic approach
 Idea: Utilize behavioral semantics specifications for semantic differencing
 Goal:
 Perform semantic differencing directly in modeling language
 Use existing behavioral semantics specifications for semantic differencing
 Enable to apply custom semantic equivalence criteria
1 S. Maoz et al. A Manifesto for Semantic Model Differencing. MODELS’10, volume 6627 of LNCS, pages 194–203. Springer, 2011.
2 U. Fahrenberg et al. Vision Paper: Make a Difference! (Semantically). MODELS’11, volume 6981 of LNCS, pages 490–500.
Springer, 2011.
18

Generic Semantic Model Differencing: Introduction
Generic framework for semantic model differencing
 Idea: Utilize behavioral semantics specifications for semantic differencing
 Procedure:
 Execute models to obtain execution traces (semantic interpretations)
 Identify execution traces valid for only one model by comparison (diff witness)
 Customize execution trace comparison to modeling language and suitable
semantic equivalence criterion
 Benefits:
 Implementation of translations and algorithms specifically for semantic model
differencing is avoided
 Only comparison of execution traces is specific to modeling language and
semantic equivalence criterion
19

Generic Semantic Model Differencing: Overview
syn
CM1,M2
M1
Syntactic
syn
Matching CM1,M2
M1 TM1
Model
Execution
Semantic
sem
Matching CM1,M2
TM1
M2
g
Match
Rules
M2
IM1 IM2
TM2
Match
Rules
g
TM2 C … Correspon-dence
I … Input
M … Model
Syn
M1 M2 Sem T … Trace
20

Epsilon Platform1 syn
CM1,M2
M1
Syntactic
syn
Matching CM1,M2
M1 TM1
Model
Execution
Semantic
sem
Matching CM1,M2
TM1
M2
g
Match
Rules
M2
IM1 IM2
TM2
Match
Rules
g
I … Input
M … Model
Syn
M1 M2 Sem
Epsilon Comparison
Language (ECL)
1 Syntactic matching: Identify syntactic correspondences
T … Trace
1.  Syntactic match rules define custom language‐specific matching algorithm
21 1 D. Kolovos, L. Rose, A. García‐Domínguez, R. Paige. The Epsilon Book. March 2014.
http://www.eclipse.org/epsilon/doc/book.

syn
CM1,M2
Behavioral semantics
M1
Syntactic
syn
Matching CM1,M2
M1 TM1
Model
Execution
Semantic
sem
Matching CM1,M2
TM1
M2
g
Match
Rules
M2
IM1 IM2
TM2
Match
Rules
g
I … Input
M … Model
Syn
M1 M2 Sem
2 Model Obtain traces
Generic execution
trace format
T … Trace
2. execution: execution  Execute models based on behavioral semantics specification
 Obtain execution traces adhering to generic execution trace format
22

Generic Execution Trace Format
states Runtime states of
model during execution
State Object
states *
t t 1
1
objects *
Runtime of
model elements
Trace
E t
source
0..1 outgoing
target incoming 0..1 Events causing
Transition
Event
qualifiedName : EString
transitions *
event 1
state transitions
 Execution Sequence Transitions between
runtime states
trace: of runtime states
 Format serves as interface to our semantic model differencing framework
 Execution traces adhering to this format are the input for indentifying semantic
differences among models
 Framework is generic with respect to semantics specification approach and
model execution environment
23

syn Epsilon Platform
CM1,M2
M1
Syntactic
syn
Matching CM1,M2
M1 TM1
Model
Execution
Semantic
sem
Matching CM1,M2
TM1
M2
g
Match
Rules
M2
IM1 IM2
TM2
Match
Rules
g
I … Input
M … Model
Syn
3 Semantic Identify semantic correspondences
Epsilon Comparison
Language (ECL)
3. matching:  Semantic match rules define whether two models are semantically equivalent
 Non‐matching traces constitute diff witnesses
24

Behavioral syn
Semantics
CM1,M2
M1
Syntactic
syn
Matching CM1,M2
M1 TM1
Model
Execution
Semantic
sem
Matching CM1,M2
TM1
M2
g
Match
Rules
M2
IM1 IM2
TM2
Match
Rules
g
I … Input
M … Model
Syn
1 1. Syntactic matching: Identify syntactic correspondences
2. Model execution: Obtain execution traces
3. Semantic matching: Identify semantic correspondences
25

Example Syntactic Match Rules (ECL)
1 rule MatchPlace
2 match left : Place with right : Place {
3 compare : left.name = right.name
4 }
5 rule MatchTransition
6 match left : Transition with right : Transition {
7 compare : left.name = right.name
8 }
Metamodel
Net
Models
M
* transitions * places
input
M1
p3
Place
name : EString
Transition
name : EString
p
*
output
p1 t1 p2 t2 p4
M2
* +
p1 t1 p2 t2 p3 t3 p4
MatchPlace MatchTransition
26

Example Behavioral Semantics (xMOF)
Behavioral semantics specification with xMOF1
 Integrates fUML action language with metamodeling languages (Ecore)
 Behavioral semantics is defined with UML activities (operational semantics)
 Model execution is performed by fUML virtual machine
27 1 T. Mayerhofer, P. Langer, M. Wimmer, and G. Kappel. xMOF: Executable DSMLs Based on fUML. In Proc. of SLE’13,
volume 8225 of LNCS, pages 56–75. Springer, 2013.

Net Transition Place
NetConfiguration TransitionConfiguration PlaceConfiguration
* heldTokens
Token
Runtime concept

TransitionConfiguration
fire()
i E bl d() EB l
PlaceConfiguration
addToken()
run() isEnabled() : EBoolean removeToken()
T k ()
NetConfiguration
main(Token[*])
()
* heldTokens
Computational steps
Token
Runtime concept
p p
for executing model

TransitionConfiguration
fire()
i E bl d() EB l
PlaceConfiguration
addToken()
run() isEnabled() : EBoolean removeToken()
Activity
T k ()
NetConfiguration
main(Token[*])
()
* heldTokens
PlaceConfiguration::addToken()
Token
Runtime concept
g ()
ReadSelf
read self
AddStructuralFeatureValue
result object
add heldTokens
j
CreateObject
1 T. Mayerhofer, P. Langer, M. Wimmer, and G. Kappel. xMOF: Executable DSMLs Based on fUML. In Proc. of SLE’13,
30
value
create Token
result

Example Execution Traces
M1 TM 1,1 objects heldTokens
t1 : Trace : PlaceConfiguration
name = “p1"
s1 : State
states
: Token
p3
p1 t1 p2
t2 p4
M0
p1
p p p
M2
+
p1 t1 p2 t2 p3 t3 p4
31

name = “p1"
s1 : State
states
: Token
p3
p1 t1 p2
objects heldTokens
t2 p4 objects
p1
s2 : PlaceConfiguration
name = “p2"
: Token
M0
p p p heldTokens
: State
: PlaceConfiguration
name = "p3"
: Token
M2
+
p1 t1 p2 t2 p3 t3 p4
32

name = “p1"
s1 : State
states
: Token
p3
p1 t1 p2
objects heldTokens
t2 p4 objects
p1
name = “p2"
: Token
M0
p p p heldTokens
: State
name = "p3"
: Token
objects : PlaceConfiguration heldTokens
s3 : State : Token
name = “p4"
M2
+
p1 t1 p2 t2 p3 t3 p4
33

name = “p1"
s1 : State
states
: Token
p3
p1 t1 p2
objects heldTokens
t2 p4 objects
p1
name = “p2"
: Token
M0
p p p heldTokens
: State
name = "p3"
: Token
s3 : State : Token
name = “p4"
states objects heldTokens
t2 : Trace s1 : State : PlaceConfiguration : Token
name = “p1"
M2
+
TM 2,1
M0
p1 t1 p2 t2 p3 t3 p4 : Token
name = “p2"
s2 : State
name = “p3"
s3 : State
objects : PlaceConfiguration heldTokens 4 St t
: Token
s4 : State : T k
Token
g
name = “p4"
34

Example Semantic Match Rules (ECL)
Final marking equivalence: For the same initial markings (input), the same
final markings are reached
1 rule MatchTrace
2 mat h l ft T ith i ht T Returns final state
capturing final marking
match left : Trace with right : Trace {
3 compare {
4 var finalStateLeft : State = left.getFinalState(); // final state of left net
5 var finalStateRight : State = right.getFinalState(); // final state of right net
6 return finalStateLeft.matches(finalStateRight) and // final states match
7 finalStateRight.matches(finalStateLeft);
8 }
9 }
Returns instances of
10 rule MatchState
PlaceConfiguration
11 match left : State with right : State {
12 compare {
13 var placeConfsLeft : Set = left getPlaceConfigurations(); // final left.states of left places
14 var placeConfsRight : Set = right.getPlaceConfigurations(); // final states of right places
15 return placeConfsLeft.matches(placeConfsRight); // final states of places match
16 }
17 }
18 rule MatchPlaceConfiguration
19 match left : PlaceConfiguration with right : PlaceConfiguration extends MatchPlace {
20 compare : left.heldTokens.size() = right.heldTokens.size() // places hold same amount of tokens
35
21 }

Example Semantic Match Rules Application
name = “p1"
s1 : State
states
: Token
p3
p1 t1 p2
objects heldTokens
t2 p4 objects
p1
name = “p2"
: Token
M0
p p p heldTokens
: State
name = "p3"
: Token
s3 : State : Token
name = “p4"
M2
+
TM 2,1 states objects heldTokens
name = “p1"
M0
p1 t1 p2 t2 p3 t3 p4
name = “p2"
s2 : State
objects : Token
: PlaceConfiguration heldTokens
name = “p3"
s3 : State : Token
j objects : PlaceConfiguration
heldTokens
s4 : State : Token
name = “p4"
36

name = “p1"
s1 : State
states
: Token
p3
p1 t1 p2
objects heldTokens
t2 p4 objects
p1
name = “p2"
: Token
M0
p p p heldTokens
: State
name = "p3"
: Token
s3 : State : Token
name = “p4"
M2
+
name = “p1"
M0
p1 t1 p2 t2 p3 t3 p4
name = “p2"
s2 : State
objects : Token
name = “p3"
s3 : State : Token
objects : PlaceConfiguration
heldTokens
MatchTrace
j s4 : State : Token
name = “p4"
37

name = “p1"
s1 : State
states
: Token
p3
p1 t1 p2
objects heldTokens
t2 p4 objects
p1
name = “p2"
: Token
M0
p p p heldTokens
: State
name = "p3"
: Token
s3 : State : Token
name = “p4"
final state
M2
+
name = “p1"
M0
p1 t1 p2 t2 p3 t3 p4
name = “p2"
s2 : State
objects : Token
MatchTrace
name = “p3"
s3 : State : Token
j objects : PlaceConfiguration
heldTokens
s4 : State : Token
name = “p4"
final state

name = “p1"
s1 : State
states
: Token
p3
p1 t1 p2
objects heldTokens
t2 p4 objects
p1
name = “p2"
: Token
M0
p p p heldTokens
: State
name = "p3"
: Token
s3 : State : Token
name = “p4"
final state
M2
+
name = “p1"
M0
p1 t1 p2 t2 p3 t3 p4
name = “p2"
s2 : State
objects : Token
MatchTrace
h
name = “p3"
s3 : State : Token
heldTokens
MatchState j s4 : State : Token
name = “p4"
final state

name = “p1"
s1 : State
states
: Token
p3
p1 t1 p2
objects heldTokens
t2 p4 objects
p1
name = “p2"
: Token
M0
p p p heldTokens
: State
name = "p3"
: Token
s3 : State : Token
name = “p4"
final state
M2
+
name = “p1"
M0
p1 t1 p2 t2 p3 t3 p4
name = “p2"
s2 : State
objects : Token
MatchTrace
j h
MatchState : PlaceConfiguration heldTokens
name = “p3"
s3 : State : Token
heldTokens
MatchPlaceConfiguration
s4 : State : Token
name = “p4"
final state

name = “p1"
s1 : State
states
: Token
p3
p1 t1 p2
objects heldTokens
t2 p4 objects
p1
name = “p2"
: Token
M0
p p p heldTokens
: State
name = "p3"
semantically : Token
equivalent
s3 : State : Token
name = “p4"
final state
M2
+
name = “p1"
M0
p1 t1 p2 t2 p3 t3 p4
name = “p2"
s2 : State
objects : Token
MatchTrace
j h
MatchState : PlaceConfiguration heldTokens
name = “p3"
s3 : State : Token
heldTokens
MatchPlaceConfiguration
s4 : State : Token
name = “p4"
final state

M1 T : M1,1 I = {} MF = {} M1,1
T : I = {p1 p1=1} MF = {p4 p4=1}
p3
M1,2 M1,2
T : I = {p2=1} MF = {p2=1} diff witness M1,3 M1,3
T : I = {p3=1} = {p3=1} diff witness p p1 t1 p p2
t2 p p4
M1,4 M1,4
MF T : M1,5 I = {p1=1,p2=1} MF = {p2=1,p4=1} diff witness M1,5
T : M1,6 I = {p1=1,p3=1} MF = {p3=1,p4=1} diff witness M1,6
T : M 7 I = {p2=1,p3=1} MF = {p4=1} diff witness M1,7 M 7 p ,p } F p } M1,7
T : M1,8 I = {p1=1,p2=1,p3=1} MF = {p4=2} diff witness M1,8
M2
+
T : M2,1 I = {} MF = {} M2,1
T : M2,2 I = {p1=1} MF = {p4=1} M2,2
T I {21} M { 4 diff it
p1 t1 p2 t2 p3 t3 p4
: M2,3 = p2=1} MF = p4=1} witness M2,3
T : M2,4 I = {p3=1} MF = {p4=1} diff witness M2,4
T : M2 5 I = {p1=1,p2=1} MF = {p4=2} diff witness M2,5 M2,5
T : M2,6 I = {p1=1,p3=1} MF = {p4=2} diff witness M2,6
T : M2,7 I = {p2=1,p3=1} MF = {p4=2} diff witness M2,7
T : I = {1 1 M = p4=3} diff M2,8 p1=1,p2=1,p3=1} MF {witness M2,8
43

Evaluation
 Case studies comparing our approach with CDDiff1 and ADDiff2 by Maoz et al.
 Behavioral semantics of both languages
 Semantic match rules for both languages
 Computation of diff witnesses for example models provided by Maoz et al.3
 Expressive power
 Sufficient for defining non‐trivial semantic model differencing operators
 Developing operators is a language engineering task
 Runtime states build basis for semantic model differencing
 Performance
 Model execution is most expensive taking 95 % of overall execution time
 High performance of execution environment and reasonable number of model
executions (inputs) are important
1 S. Maoz et al. CDDiff: Semantic Differencing for Class Diagrams. ECOOP’11, volume 6813 of LNCS, pages 230–254. Springer, 2011.
2 S. Maoz et al. ADDiff: Semantic Differencing for Activity Diagrams. ESEC/FSE’11, pages 179–189. ACM, 2011.
44
3 http://www.se‐rwth.de/materials/semdiff

Summary
syn
CM1,M2
M1
Syntactic
syn
Matching CM1,M2
M1 TM1
Model
Execution
Semantic
sem
Matching CM1,M2
TM1
M2
g
Match
Rules
M2
IM1 IM2
TM2
Match
Rules
g
I … Input
M … Model
Syn
Characteristics
 Generic w.r.t. modeling language (behavioral semantics)
 Generic w.r.t. semantics specification approach (generic trace format)
 Configurable w.r.t. semantic equivalence criterion (semantic match rules)
45

Outlook
 Generation of inputs relevant to semantic differencing
 Relevant inputs are inputs that cause distinct execution traces
 Problem: Inputs have to be defined manually now
 Symbolic execution1
 Calculation of semantic differences avoiding model execution
 Problem: Model execution for obtaining concrete execution traces is expensive
 Path conditions obtained from symbolic execution already capture differences
among execution traces
 Only syntactic differences can lead to semantic differences
 Directed and differential symbolic execution2,3
46
1 L. Clarke. A Program Testing System. In Proc. of ACM ‘76, pages 488–491. ACM, 1976.
2 K.‐K. Ma et al. Directed Symbolic Execution. In Proc of SAS’11, volume 6887 of LNCS, pages 95–111. Springer, 2011.
3 S. Person et al. Differential Symbolic Execution. In Proc. of FSE’08, pages 226–237. ACM, 2008.

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