semantic based model matching with emf compare

Dipartimento di Ingegneria e Scienze

Università degli Studi dell’Aquila

dell’Informazione e Matematica

Semantic-based Model Matching with

EMFCompare

Davide Di Ruscio

[email protected]

@ddiruscio

Models and Evolution Workshop at MoDELS 2016 – October 2, 2016 – Saint-Malo, France

ME‘16 – October 2, 2016 – Saint-Malo, France

2Joint work with

Alfonso PierantonioUnversity of L’Aquila

(Italy)

Ludovico IovinoGran Sasso Science Institute

(Italy)

Juri Di RoccoUnversity of L’Aquila

(Italy)

Lorenzo AddaziMalardalen University

(Sweden)

Antonio CicchettiMalardalen University

(Sweden)


3Introduction

Model comparison is one of the most challenging

operations in MDE

It underpins a wide range of modelling activities

• E.g., model versioning, evolution, collaborative modeling, …

Calculating model differences relies on the model

matching problem

• It can be reduced to the problem of finding correspondences between

two given graphs (Graph Isomorphism Problem, NP-Hard)


4Introduction

a

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5Introduction

a

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dVersion 1

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Establish

correspondences

Calculate

differences


6Introduction

a

b

f

c

e

dVersion 1

a

k

l

c

e

dVersion 2

m

Establish

correspondences

Calculate

differences


7Introduction

a

b

f

c

e

dVersion 1

a

k

l

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dVersion 2

m

Establish

correspondences

Calculate

differences

> Rename node b as k

> Rename node f as l

> Add node m

> Add edge from k to m


8Model-matchingStatic Identity-Based Matching: each model element has a persistent unique identifier that is assigned to it upon creation

Signature-Based Matching: the identifier of each model element is dynamically calculated by combining the values of its features

Similarity-Based Matching: models are typed attribute graphs and matching elements are identified by considering the aggregated similarity of their features.

Language-Specific Matching: matching algorithms are tailored to a particular modelling language


9

Similiartiy-based matching

Extensible

• Static identity-based or signature-based matching can be also added

by defining custom generator functions


10

The default match engine

The Levenshtein distance algorithm is applied on the

string representation of the elements

• For optimisation purposes the models are compared by considering

elements selected within a proper search window

...

foreach (elM1 : Model1.getElements())

foreach (elM2 : elM1.getWindowElements())

result[elM1][elM2] = calculateSimilarity(elM1, elM2)

return createMatches(result)

...


11A meta-model evolution scenario

A University theses management metamodel




Extract super class




Attribute renaming


17

Contextual issues: limited consideration of the

features characterising the elements

surrounding/containing the compared one

Linguistic issues: lack of semantical evaluation of

the features characterizing the compared elements

• False-negative e.g., renaming a given class using a syntactically

different name

• False-positive e.g., renaming a given class using a semantically

different term, which however presents a strong syntactical

similarity


19Proposed approach

Semantic Match Engine

• Use of the WordNet lexical dictionary as ontological source


20WordNet in a nutshell

Lexical database for the English language

English words are grouped into sets of synonyms

(synsets)

Each synset includes

- a generic definition joining the contained words

- semantic relationships connecting it to other synsets

http://www.cs.princeton.edu/courses/archive/fall16/cos226/assignments/wordnet.html


21WordNet in a nutshell

Lexical database for the English language

English words are grouped into sets of synonyms

(synsets)

Each synset includes

- a generic definition joining the contained words

- semantic relationships connecting it to other synsets

http://www.cs.princeton.edu/courses/archive/fall16/cos226/assignments/wordnet.html


22The proposed

semantic model matching

function createMatches(Comparison comparison, List

leftEObjects, List rightEObjects){

SemanticMatch root = createSemanticMatch(null, null);

exploreMatches(root, leftEObjects, rightEObjects);

evaluateMatches(root);

filterMatches(root, comparison);

}

Exploration

Evaluation

Filtering


23The proposed








}

Exploration

Evaluation

Filtering

A labelled graph representation of the compared models is produced• each node represents a semantic match• each incoming or outgoing labelled edge

represents a connection with its parents or children elements


24

Type: EAttribute

Source: Student.usernameTarget: User.password

Sim: null

Type: EClass

Source: StudentTarget: User

Sim: null

Type: EClass

Source: StudentTarget: Student

Sim: null

Type: EAttribute

Source: Student.passwordTarget:User.password

Sim: nullType: EAttribute

Source: Student.passwordTarget: User.username

Sim: nullType: EAttribute

Source: Student.usernameTarget: User.username

Sim: null


25The proposed








}

Exploration

Evaluation

Filtering

Each SemantichMatch node is integrated with the semantic distance value between the encapsulated element


26

Type: EAttribute


Sim: 0.2

Type: EClass


Sim: 0.4

Type: EClass


Sim: 1

Type: EAttribute


Sim: 0.6Type: EAttribute

Source: Student.passwordTarget: User.username



Sim: 0.6


27The proposed








}

Exploration

Evaluation

Filtering

The set of SemanticMatch elements are filtered out with respect to a predefined threshold


28

Type: EAttribute


Sim: 0.2

Type: EClass


Sim: 0.4

Type: EClass


Sim: 1

Type: EAttribute



ource: Student.passwordTarget: User.username



Sim: 0.6


29Experiments

The Model Exchange Benchmark

• 5 structural modelling languages

• All the possible pairs of metamodels are given as input to:

• Semantic EMFCompare

• EMFCompare

• GAMMA(*)

• Coma++, FOAM, Crosi, Alignment API, AMW

(*) M. Kessentini, A. Ouni, P. Langer, M. Wimmer, and S. Bechikh, “Search-based metamodel matching

with structural and syntactic measures,” J. Syst. Softw., vol. 97, no. C, pp. 1–14, Oct. 2014.


30Experiments

Measures


31Experiments

Measures

It denotes the percentage of correctly matched elements

with respect to all the proposed matches


32Experiments

Measures

It denotes the percentage of correctly matched elements

with respect to all the expected matches


33Experiments

Measures It combines Precision and Recall to get an equally

weighted average value of the measures


34Experiments

GAMMA provides best results with respect to Precision,

Recall, and F-Measure

GAMMA uses SBSE approaches and it requires to be

initialized with a set of initial solutions (knowledge base)


35Experiments

Semantic EMFCompare:

• produces more matches than expected

• in some cases has lower Precision than EMFCompare

• only in one case F-Measure is lower than EMFCompare


36Experiments


37Lessons learnt

Extending EMFCompare with semantic aspects can be done in a lightweight manner

An increasing matching power can come at the price of an increasing imprecision (more false-positives and false-negatives)

The selection of the appropriate dictionary (depending on the artifacts to be compared) can make the difference• Comparing metamodels is semantically different than comparing models

of specific domains

Performing experiments can be an issue due to the lack of models to be used as test cases

• Existing model mutations approaches should be extended to implement “semantics-aware” mutations


38Conclusion and Future Work

Model comparison is a very complex task

It underpins the management of a wide number of

(meta-)model (co-)evolution scenarios

An extension of the EMFCompare tool has been

proposed to enable “semantics-aware” matches

Further experiments will be performed by considering

the application of different dictionaries depending on

the kinds of artifacts to be matched


39

semantic based model matching with emf compare

Software