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

Semantic Model Differencing Utilizing Behavioral Semantics Specifications

www.modelexecution.orgg

Philip Langer, Tanja Mayerhofer, Gerti Kappel

Business Informatics GroupInstitute of Software Technology and Interactive Systems Vienna University of Technology

p g , j y , pp

Favoritenstraße 9‐11/1883, 1040 Vienna, Austriaphone: 43 (1) 5880118804 (secretary), fax: 43 (1) [email protected], www.big.tuwien.ac.at

MotivationMotivation

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 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 DifferencingMotivating Example Syntactic Model Differencing

M1

p3

t1p1 p2 p4t2

M2

t1p1 p2 p3t2 t3 p4

+

t1p1 p2 p3t2 t3 p

3


1. MatchingM1

Identify corresponding elements based on Identifiers

p3

Identifiers Signatures Similarity

t1p1 p2 p4t2

Custom matchingM2

t1p1 p2 p3t2 t3 p4

+

t1p1 p2 p3t2 t3 p

4


1. MatchingM1


p3


t1p1 p2 p4t2

Custom matching (names)M2

t1p1 p2 p3t2 t3 p4

+

t1p1 p2 p3t2 t3 p

5


1. MatchingM1


p3


t1p1 p2 p4t2

Custom matching (names)

2. DifferencingM2

Compare corresponding elements

t1p1 p2 p3t2 t3 p4

+

Identify non‐corresponding elements

t1p1 p2 p3t2 t3 p

6


1. MatchingM1


p3


t1p1 p2 p4t2

p3 removed as outputp3 removed as inputp3 added as output


2. DifferencingM2

!

p3 removed as output p3 added as output p4 removed as output

! Compare corresponding

elementst1p1 p2 p3t2 t3 p4

++! !


t1p1 p2 p3t2 t3 p

t3 added

7


Which impact do the changes have on the model(‘s semantics)?

1. MatchingM1

Which impact do the changes have on the model( s semantics)?


p3


t1p1 p2 p4t2

p3 removed as outputp3 removed as inputp3 added as output


2. DifferencingM2

!

p3 removed as output p3 added as output p4 removed as output

! Compare corresponding

elementst1p1 p2 p3t2 t3 p4

++! !


t1p1 p2 p3t2 t3 p

t3 added

8

Introduction to Semantic Model DifferencingIntroduction to Semantic Model Differencing

Semantics of a modeling language1

Mapping : Language → Semantic domain Assigns meaning to each conformant model Provides (semantic) interpretations of models

i d l diff iSemantic model differencing Procedure:

Obtain semantic interpretations of models Obtain semantic interpretations of models Analyze semantic interpretations

Output: Semantic differencesp 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 DifferencingMotivating Example Semantic Model Differencing

Petri netsSemantics Firing of transitions leading to markings

(token distributions)M1

(token distributions)

Semantic interpretation

p3

Semantic interpretation Traces (order of transition firings) Reachable markings

t1p1 p2 p4t2

Semantic differencel l d d l

M2

Traces only valid in one model Markings only reachable in one model Final markings only reached by one modelt1p1 p2 p3t2 t3 p4

+

Final markings only reached by one modelt1p1 p2 p3t2 t3 p

10


Petri netsSemantics Firing of transitions leading to markings

(token distributions)M1

(token distributions)

Semantic interpretation

p3

Semantic interpretation Traces (order of transition firings) Reachable markings

t1p1 p2 p4t2

Semantic differencel l d d l

M2

Traces only valid in one model Markings only reachable in one model Final markings only reached by one modelt1p1 p2 p3t2 t3 p4

+

Final markings only reached by one modelt1p1 p2 p3t2 t3 p

11


Petri netsSemantic interpretation

M1

M : p1=1 p2=0 p3=0 p4=0p3

M0,M1: p1=1, p2=0, p3=0, p4=0

M0

t1p1 p2 p4t2

M2

t1p1 p2 p3t2 t3 p4

+

t1p1 p2 p3t2 t3 p

12



M1

M : p1=1 p2=0 p3=0 p4=0p3

M0,M1: p1=1, p2=0, p3=0, p4=0M1,M1: p1=0, p2=1, p3=1, p4=0

M0

t1p1 p2 p4t2

M2

t1p1 p2 p3t2 t3 p4

+

t1p1 p2 p3t2 t3 p

13



M1

M : p1=1 p2=0 p3=0 p4=0p3

M0,M1: p1=1, p2=0, p3=0, p4=0M1,M1: p1=0, p2=1, p3=1, p4=0M2,M1: p1=0, p2=0, p3=0, p4=1M0

t1p1 p2 p4t2

M2

t1p1 p2 p3t2 t3 p4

+

t1p1 p2 p3t2 t3 p

14



M1

M : p1=1 p2=0 p3=0 p4=0p3


t1p1 p2 p4t2

M2 M0,M2: p1=1, p2=0, p3=0, p4=0M : p1=0 p2=1 p3=0 p4=0

t1p1 p2 p3t2 t3 p4

+M1,M2: p1=0, p2=1, p3=0, p4=0M2,M2: p1=0, p2=0, p3=1, p4=0M3,M2: p1=0, p2=0, p3=0, p4=1

M0

t1p1 p2 p3t2 t3 p

15


Petri netsSemantic difference (final marking)

M1

M : p1=1 p2=0 p3=0 p4=0p3


t1p1 p2 p4t2semanticallyequivalent

M2 M0,M2: p1=1, p2=0, p3=0, p4=0M : p1=0 p2=1 p3=0 p4=0

q

t1p1 p2 p3t2 t3 p4

+M1,M2: p1=0, p2=1, p3=0, p4=0M2,M2: p1=0, p2=0, p3=1, p4=0M3,M2: p1=0, p2=0, p3=0, p4=1

M0

t1p1 p2 p3t2 t3 p

16


Petri netsSemantic difference (final marking)

M1

M : p1=0 p2=1 p3=0 p4=0p3

M0,M1: p1=0, p2=1, p3=0, p4=0

M0

t1p1 p2 p4t2 diff witness

M2 M0,M2: p1=0, p2=1, p3=0, p4=0M : p1=0 p2=0 p3=1 p4=0

t1p1 p2 p3t2 t3 p4

+M1,M2: p1=0, p2=0, p3=1, p4=0M2,M2: p1=0, p2=0, p3=0, p4=1M0

t1p1 p2 p3t2 t3 p

17

Generic Semantic Model Differencing: MotivationGeneric Semantic Model Differencing: Motivation

Language‐specific approach (Maoz et al.1 and Fahrenberg et al.2) Procedure:

Translate models into semantic domainP f ti diff i i ti d i Perform semantic differencing in semantic domain

Translate result into modeling language Challenge: Complex translations and differencing algorithmsChallenge: Complex translations and differencing algorithms

Generic approachId U ili b h i l i ifi i f i diff i Idea: Utilize behavioral semantics specifications for semantic differencing

Goal: Perform semantic differencing directly in modeling language Perform semantic differencing directly in modeling language Use existing behavioral semantics specifications for semantic differencing Enable to apply custom semantic equivalence criteria

181 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.

Generic Semantic Model Differencing: IntroductionGeneric 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 suitableCustomize 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 andOnly comparison of execution traces is specific to modeling language and semantic equivalence criterion

19

Generic Semantic Model Differencing: OverviewGeneric Semantic Model Differencing: Overview

CM1,M2syn

M1

Syntactic Matching CM1,M2

syn Model

Execution

M1 TM1

Semantic Matching CM1,M2

sem

TM1

M2

g

Match Rules

M2

IM1 IM2

TM2

Match Rules

g

TM2 C … Correspon-dence

I … InputM … Model

SynM1 M2

Sem T … Trace

20


CM1,M2syn Epsilon Platform1

M1


syn Model

Execution

M1 TM1


sem

TM1

M2

g

Match Rules

M2

IM1 IM2

TM2

Match Rules

g



1 Syntactic matching: Identify syntactic correspondences

SynM1 M2

SemEpsilon Comparison Language (ECL)

T … Trace

1. Syntactic matching: Identify syntactic correspondences Syntactic match rules define custom language‐specific matching algorithm

211 D. Kolovos, L. Rose, A. García‐Domínguez, R. Paige. The Epsilon Book. March 2014. http://www.eclipse.org/epsilon/doc/book.


CM1,M2syn

Behavioral semantics

M1


syn Model

Execution

M1 TM1


sem

TM1

M2

g

Match Rules

M2

IM1 IM2

TM2

Match Rules

g



2 Model execution: Obtain execution traces

SynM1 M2

SemGeneric execution trace format

T … Trace

2. Model execution: Obtain execution traces Execute models based on behavioral semantics specification Obtain execution traces adhering to generic execution trace format

22

Generic Execution Trace FormatGeneric Execution Trace Format

Runtime states of

Runtime states of model during execution

State Objectstates *

1t t 1

objects *

Runtime states of model elements

Trace

E t

source1

outgoing0..1

target 1

incoming 0..1 Events causingstate transitions

TransitionEvent

qualifiedName : EStringtransitions *

event 1

Transitions between

state transitions

Execution trace: Sequence of runtime states

Transitions between runtime states

Execution trace: Sequence 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 g p y gdifferences among models

Framework is genericwith respect to semantics specification approach and model execution environmentmodel execution environment

23


CM1,M2syn Epsilon Platform

M1


syn Model

Execution

M1 TM1


sem

TM1

M2

g

Match Rules

M2

IM1 IM2

TM2

Match Rules

g



3 Semantic matching: Identify semantic correspondences

SynM1 M2

Sem T … Trace

Epsilon Comparison Language (ECL)

3. Semantic matching: Identify semantic correspondences Semantic match rules define whether two models are semantically equivalent Non‐matching traces constitute diff witnesses

24


CM1,M2syn Behavioral

Semantics

M1


syn Model

Execution

M1 TM1


sem

TM1

M2

g

Match Rules

M2

IM1 IM2

TM2

Match Rules

g



1 Syntactic matching: Identify syntactic correspondences

SynM1 M2

Sem T … Trace

1. Syntactic matching: Identify syntactic correspondences2. Model execution: Obtain execution traces3. Semantic matching: Identify semantic correspondences3. Semantic matching: Identify semantic correspondences

25

Example Syntactic Match Rules (ECL)Example Syntactic Match Rules (ECL)

1 rule MatchPlace2 match left : Place with right : Place {2 match left : Place with right : Place {3 compare : left.name = right.name4 }

5 rule MatchTransition6 match left : Transition with right : Transition {7 compare : left.name = right.name8 }

MetamodelNet

Models

M

inputtransitions* places*

M1

p3

Place

name : EString

Transition

name : EString

p

*output

* +

t1p1 p2 p4t2M2

+

t1p1 p2 p3t2 t3 p4

MatchPlace MatchTransition

26

MatchPlace MatchTransition

Example Behavioral Semantics (xMOF)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

271 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.




Transition PlaceNet

TransitionConfiguration PlaceConfigurationNetConfiguration

heldTokens*

Token

Runtime concept





Transition PlaceNet

TransitionConfiguration

fire()i E bl d() EB l

PlaceConfiguration

addToken()T k ()

NetConfiguration

main(Token[*])()

heldTokens*

isEnabled() : EBoolean removeToken()run()

Computational stepsToken

Runtime concept

p pfor executing model





Transition PlaceNet

TransitionConfiguration

fire()i E bl d() EB l

PlaceConfiguration

addToken()T k ()

NetConfiguration

main(Token[*])()

heldTokens*

isEnabled() : EBoolean removeToken()run()

ActivityPlaceConfiguration::addToken()

Token

Runtime concept

g ()

read selfReadSelf

result add heldTokens

AddStructuralFeatureValue

objectCreateObject


30value

create Tokenj

result

Example Execution TracesExample Execution Traces

M1 T M1,1 objects heldTokenst1 : Trace : PlaceConfiguration

name = “p1"s1 : State

states: Token

t1p1 p2

p3

p4t2

M0

name p1

p p p

M2

+

t1p1 p2 p3t2 t3 p4

31




states: Token

t1p1 p2

p3

p4t2objects

objects heldTokens

name p1

s2 : State

: PlaceConfiguration

name = “p2": Token

M0

p p pobjects

heldTokenss2 : State


name = "p3": Token

M2

+

t1p1 p2 p3t2 t3 p4

32




states: Token

t1p1 p2

p3

p4t2objects

objects heldTokens

name p1

s2 : State



M0

p p pobjects



name = "p3": Token

objects heldTokens: PlaceConfiguration

name = “p4"s3 : State : Token

M2

+

t1p1 p2 p3t2 t3 p4

33




states: Token

t1p1 p2

p3

p4t2objects

objects heldTokens

name p1

s2 : State



M0

p p pobjects



name = "p3": Token



states objects heldTokenst2 : Trace s1 : State : Token: PlaceConfiguration

name = “p1"

M2

+

T M2,1

M0objects heldTokens: PlaceConfiguration


objects heldTokens

: Tokent1p1 p2 p3t2 t3 p4

M0

objects heldTokens: PlaceConfiguration4 St t



T k

: Token

g


34

Example Semantic Match Rules (ECL)Example Semantic Match Rules (ECL)

Final marking equivalence: For the same initial markings (input), the same final markings are reached

1 rule MatchTrace2 mat h l ft T ith i ht T {

Returns final state capturing final marking

2 match left : Trace with right : Trace {3 compare {4 var finalStateLeft : State = left.getFinalState(); // final state of left net5 var finalStateRight : State = right.getFinalState(); // final state of right net6 return finalStateLeft matches(finalStateRight) and // final states match6 return finalStateLeft.matches(finalStateRight) and // final states match7 finalStateRight.matches(finalStateLeft);8 }9 }

Returns instances of10 rule MatchState11 match left : State with right : State {12 compare {13 var placeConfsLeft : Set = left getPlaceConfigurations(); // final states of left places

Returns instances of PlaceConfiguration

13 var placeConfsLeft : Set = left.getPlaceConfigurations(); // final states of left places14 var placeConfsRight : Set = right.getPlaceConfigurations(); // final states of right places15 return placeConfsLeft.matches(placeConfsRight); // final states of places match16 }17 }}

18 rule MatchPlaceConfiguration19 match left : PlaceConfiguration with right : PlaceConfiguration extends MatchPlace {20 compare : left.heldTokens.size() = right.heldTokens.size() // places hold same amount of tokens

35

20 compare : left.heldTokens.size() right.heldTokens.size() // places hold same amount of tokens21 }

Example Semantic Match Rules ApplicationExample Semantic Match Rules Application



states: Token

t1p1 p2

p3

p4t2objects

objects heldTokens

name p1

s2 : State



M0

p p pobjects



name = "p3": Token



M2

+

T M2,1 states objects heldTokenst2 : Trace s1 : State : Token: PlaceConfiguration

name = “p1"M0

t1p1 p2 p3t2 t3 p4objects heldTokens: PlaceConfiguration


objects heldTokens

: Token

M0




j : PlaceConfiguration


36




states: Token

t1p1 p2

p3

p4t2objects

objects heldTokens

name p1

s2 : State



M0

p p pobjects



name = "p3": Token



M2

+


name = “p1"M0



objects heldTokens

: Token

M0




MatchTracej : PlaceConfiguration


37




states: Token

t1p1 p2

p3

p4t2objects

objects heldTokens

name p1

s2 : State



M0

p p pobjects



name = "p3": Token



final state

M2

+


name = “p1"M0



objects heldTokens

: Token

M0

MatchTraceobjects heldTokens: PlaceConfiguration



j : PlaceConfiguration


final state




states: Token

t1p1 p2

p3

p4t2objects

objects heldTokens

name p1

s2 : State



M0

p p pobjects



name = "p3": Token



final state

M2

+


name = “p1"M0



objects heldTokens

: Token

M0

h




MatchStatej : PlaceConfiguration


final state




states: Token

t1p1 p2

p3

p4t2objects

objects heldTokens

name p1

s2 : State



M0

p p pobjects



name = "p3": Token



final state

M2

+


name = “p1"M0



objects heldTokens

: Token

M0

h




MatchPlaceConfiguration



final state




states: Token

t1p1 p2

p3

p4t2objects

objects heldTokens

name p1

s2 : State



M0

p p pobjects



name = "p3": Token



final state

M2

+


name = “p1"M0



objects heldTokens

: Token

M0

h







final state




states: Token

t1p1 p2

p3

p4t2objects

objects heldTokens

name p1

s2 : State



M0

p p pobjects



name = "p3": Tokensemantically

equivalentobjects heldTokens: PlaceConfiguration


final state

M2

+


name = “p1"M0



objects heldTokens

: Token

M0

h







final state


M1 T :M1,1 I = {} MF = {}M1,1T : I = {p1=1} MF = {p4=1}

t1p1 p2

p3

p4t2

T :M1,2 I {p1 1} MF {p4 1}M1,2T :M1,3 I = {p2=1} MF = {p2=1} diff witnessM1,3T :M1,4 I = {p3=1} MF = {p3=1} diff witnessM1,4p p pT :M1,5 I = {p1=1,p2=1} MF = {p2=1,p4=1} diff witnessM1,5T :M1,6 I = {p1=1,p3=1} MF = {p3=1,p4=1} diff witnessM1,6T :M 7 I = {p2=1,p3=1} MF = {p4=1} diff witnessM 7M1,7 {p ,p } F {p }M1,7T :M1,8 I = {p1=1,p2=1,p3=1} MF = {p4=2} diff witnessM1,8

M2

+

T :M2,1 I = {} MF = {}M2,1T :M2,2 I = {p1=1} MF = {p4=1}M2,2T I { 2 1} M { 4 1} diff it

t1p1 p2 p3t2 t3 p4T :M2,3 I = {p2=1} MF = {p4=1} diff witnessM2,3T :M2,4 I = {p3=1} MF = {p4=1} diff witnessM2,4T :M2 5 I = {p1=1,p2=1} MF = {p4=2} diff witnessM2 5M2,5 M2,5T :M2,6 I = {p1=1,p3=1} MF = {p4=2} diff witnessM2,6T :M2,7 I = {p2=1,p3=1} MF = {p4=2} diff witnessM2,7T : I = {p1=1 p2=1 p3=1} M = {p4=3} diff witnessT :M2,8 I = {p1=1,p2=1,p3=1} MF = {p4=3} diff witnessM2,8

43

EvaluationEvaluation

Case studies comparing our approach with CDDiff1 and ADDiff2 by Maoz et al. Behavioral semantics of both languages Semantic match rules for both languages

C t ti f diff it f l d l id d b M t l 3 Computation of diff witnesses for example models provided by Maoz et al.3

Expressive powerffi i f d fi i i i l i d l diff i Sufficient for defining non‐trivial semantic model differencing operators

Developing operators is a language engineering task Runtime states build basis for semantic model differencingRuntime states build basis for semantic model differencing

Performance Model execution is most expensive taking 95 % of overall execution time 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

441 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, 2011.3 http://www.se‐rwth.de/materials/semdiff

SummarySummary

CM1,M2syn

M1


syn Model

Execution

M1 TM1


sem

TM1

M2

g

Match Rules

M2

IM1 IM2

TM2

Match Rules

g



Characteristics

SynM1 M2

Sem T … Trace

Characteristics Generic w.r.t. modeling language (behavioral semantics) Generic w.r.t. semantics specification approach (generic trace format)Generic w.r.t. semantics specification approach (generic trace format) Configurable w.r.t. semantic equivalence criterion (semantic match rules)

45

OutlookOutlook

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 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 y y p

among execution traces Only syntactic differences can lead to semantic differences

Directed and differential symbolic execution2,3

461 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.

Thank you!Thank you!

Model Execution Based on fUML

www.modelexecution.org

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

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