generic and meta-transformations for model transformation engineering

Budapest University of Technology and EconomicsDepartment of Measurement and Information Systems

Generic and Meta-Transformations for Model Transformation Engineering

Dániel Varró, András PatariczaBudapest University of Technology and Economics

Most Influential Paper Presentation@MODELS 2014 Valencia, Spain, October 2nd, 2014

2

Generic&Meta40%

VIATRA27%

MT Engineering10%

Other6%

Self8%

None9%

Citations

Contributions of our UML 2004 paper Novel MT Concepts

o Generic and o Meta-Transformations

Highlight the software engineering aspects of MTo Reusabilityo Maintainabilityo Performanceo Compactness

1st prototype of VIATRA2 transformation framework

Impact (Citation) Analysiso Total: 137 (Google Scholar)

3

Overview

VIATRA History

Outlook on Generic and Meta-Transformations

UML 2004 talk

Generic and Meta-Transformations

for

Model Transformation Engineering

Dániel VarróAndrás Pataricza

Budapest Univ. of Technology and Economics

Dept. of Measurement and Information Systems

(Slides with this design are taken from my UML 2004 talk)

October 14th, 2004 UML 2004

The Model Transformation Problem

Metamodel A

Language A Language B

Metamodel B

Model a Model b

How to specify?

How to execute?conforms to conforms to

UML DB

Platform Indep. Trans. (PIT)

J. Bézivin, N. Farcet, J.-M. Jézéquel, B. Langlois, and D. Pollet. Reflective model driven engineering. In Proc. UML 2003: 6th Int. Conf. on the Unified Modeling Language, LNCS 2863, pp. 175–189. Springer, San Francisco, CA, USA, 2003

Solution 1: Manually written PSTs

Metamodel A


Metamodel B

Model a Model b

conforms to conforms to

UML DB

Schema SA Schema SA

Document da Document db

XSLT spec. XT

XSLT exec. xt


Platform Spec. Trans. (PST)

Platform Indep. Trans. (PIT)

Solution 1: Manually written PSTs

Metamodel A


Metamodel B

Model a Model b


UML DB

Schema SA Schema SA


XSLT spec. XT

XSLT exec. xt


Platform Spec. Trans. (PST)

MOF technological space

Advantages:+ standard model format (XMI) and transform. language (XSLT)+ extensive tool support (XSLT engines)Disadvantages:– knowledge is required in another technological space (XSLT)– hard to debug/maintain/understand complex XSLT scripts – XSLT performance problems (for graph-like structures, e.g. XMI)


Solution 2: PIT Specification

Metamodel A


Metamodel B

Model a Model b

Transf. Spec. T


UML DB

QVT


Solution 2: PIT specification + engine

Metamodel A


Metamodel B

Model a Model b

Transf. Spec. T

Transf. Exec. t


UML DB


QVT example

C:Class

:columns

A:Attribute

name=NMtype=TP

:attrs

B:Column

name=NMtype=TP

Cls2Tbl(C,T)

T:Table

Attr2Col(A,B)

Attr2ColumnR

when

Condition

RelationQVT rule

Pattern

Variable


Graph transformation example

C:Class

A:Attribute

name=NMtype=TP

:attrs

T:Table

Cls2Tbl(C,T)

LHS

C:Class

:columns

A:Attribute

name=NMtype=TP

:attrs

B:Column

name=NMtype=TP

T:Table

Cls2Tbl(C,T)

RHS

Att2Col(A,B)


Solution 2: PIT specification + engine

Metamodel A


Metamodel B

Model a Model b

Transf. Spec. T

Transf. Exec. t


UML DB

Advantages:+ pattern-based transformation specification in UML/MOF (upcoming QVT standard)+ increased performance for complex graph modelsDisadvantages: – lack of (industrial) model transformation tools


Solution 3: Model-driven transformation of PSMs

Metamodel A


Metamodel B

Model a Model b


UML DB

Schema SA Schema SA



Transf. Spec. T

Transf. Exec. t


Solution 3: Model-driven transformation of PSMs

Metamodel A


Metamodel B

Model a Model b


UML DB

Schema SA Schema SA


XSLT spec. XT

XSLT exec. xt


Transf. Spec. T

Transf. Exec. t

Advantages: (as before)+ pattern-based transformation specification in UML/MOF (upcoming QVT standard)+ increased performance for complex graph models (vs. XSLT)Disadvantages: – HOW TO INTEGRATE INTO EXISTING TOOLS?

Solution 4: Automatically generated PST

Metamodel A


Metamodel B

Model a Model b


UML DB

Schema SA Schema SA



Transf. Spec. T

XSLT spec. XT

XSLT exec. xt

Meta-transformation

Solution 4: Automatically generated TST

Metamodel A


Metamodel B

Model a Model b


UML DB

Schema SA Schema SA



Transf. Spec. T

XSLT spec. XT

XSLT exec. xt

Advantages:+ transformation specification in QVT/MOF/UML+ automatically generated XSLT scripts+ platform specific transformers (integrated into the target technological space)Disadvantages:– PIT2PST is a complex meta-transformation (e.g. QVT2XSLT)– we have to do it ONCE / we HAVE TO do it once


Transformation engineering in MDA

Transformation design will be an engineering principle (if MDA succeeds)

Questions to answer: – How to specify and execute MTs?– Compactness / Intuitiveness of MT

Specs? – Automation? - Performance?– Analysis? - Correctness of

transformations?– Reusability? - Maintainability?


Objectives

How to tackle (some) transware problems?– By storing/handling transformations

as models

Generic (higher-order) transformations:– Type / class variables in rules

Meta transformations:– Consumes (produces) transformation

rules as input (output)

Higher-Order Transformations


Example: XMI export

Problem: – generate XMI

documents – from MOF-based

models– when the metamodel

is a parametername = First

isStart = true

n1 : Node

name = SecondisStart = false

n2 : Node

weight = 1

e1 : Edge

outgoing

incoming

name : StringisStart : Boolean

Node

weight : Integer

Edge

subnodes

outgoing

incoming

Graph

<Node xmi.id = "n1"> <Node.name>FirstNode</Node.name> <Node.isStart xmi.value="true" /> <Node.outgoing> <Edge xmi.idref = "e1"/> </Node.outgoing> <Node.subnodes> <Node xmi.id = "n2"> ... </Node> </Node.subnodes></Node>


Overview of generic transformations

Advantages– General +

Powerful– Succinct

Disadvantages– Performance?– Implementation

as PSTs?

Metamodel(Graphs)

Graph model

Generic Trans. Eng.

Generic rules

XMI document

Generic transformation = Rules with type variables


Example: A generic rule

identifier = id

o:x

name = n

x:Class

name = n

e:XMLElem

name = "xmi.id"value = id

a:XMLAttr

:attributesObj2Elem(o,e)

{new}

{new}

{new}

{new}

Obj2ElemR

Type paramete

r


Overview of meta-transformations

Advantage:– Executed by

traditional transformation engines

Prerequisite:– Rules stored as

modelsMetamodel(Graphs)

Graph model

“Ordinary” Trans. Eng.

Meta rules

XMI document


“Ordinary” rules

Meta-Transformation = Rules that consume/generate rules

Transformation Models


Example: A meta rule

name = at

att:Attribute

name = cl

x:Class

as:attributes

isEnum = "true"

y:Class

:type

EnumAttr2ElemR(x,att)

value = v

a:att

o:x

:as{new}

{new} :contains

e1:XMLElem

name = cl+"."+at

e2:XMLElem

name = "xmi.value"value = v

a2:XMLAttr

:attributes

Attr2Elem(a,e2)

Obj2Elem(o,e1)

{new}{new}

{new}

Attr2Rule

Attr2RuleR

Type variables are data in the RHS resolved prior to main transformation


Result of applying a meta rule

value = v

a:weight

o:Edge

:as{new}

{new} :contains

e1:XMLElem

name = "Edge.weight"

e2:XMLElem


a2:XMLAttr

:attributes{new}{new}

Attr2Elem(a,e2)

EnumAttr2ElemR(“Edge”,”weight”)

{new}

Obj2Elem(o,e1)

Ordinary (first order) transformation rule without type variables


Generic rules for model maintenance

a:att:Attribute

:contains

e1:XMLElem

name = n

e2.XMLElem


a1:XMLAttr

:attributes

Attr2Elem(a,e2)

{delete}name = nvalue = v

a2:XMLAttr

:contains

Attr2Attr(a,a2)

{new}

{delete}{delete}

{delete}

{delete}

{new}

{new}

ChangeXMIAttributesR

Migration from XMI 1.0 to XMI 1.1


Another scenario of generic and meta-transformations

Advantage: uniform management of– models– transformations“Ordinary”

rules

Graph model

Generic Trans. Eng.

Generic & Meta rules

XMI document



Meta-Transformation = Rules that consume/generate rulesGeneric Transformation = Rules with type variables

The VIATRA2 Approach Model management:

o Model space: Unified, global view of models, metamodels and transformations

• Hierarchical graph model • Complex type hierarchy• Multilevel metamodeling

Model manipulation and transformations: integration of two mathematically precise, rule and pattern-based formalisms o Graph patterns (GP): structural conditionso Graph transformation (GT): elementary transformation steps o Abstract state machines (ASM): complex transformation programs

Code generation:o Special model transformations with o Code templates and code formatters

precondition pattern lhs(Par,Child,P1,ClsE,ParR,AncR) =

{ entity(ClsE); relation(ParR,ClsE,ClsE); relation(AncR,ClsE,ClsE); entity(Par); entity(Child);

relation (P1, Child,Par); instanceOf(Par, ClsE); instanceOf(Child, ClsE); instanceOf(P1, ParR);} …

Generic GT rules in VIATRA Explicit (dynamic)

instanceOf relations Variables matched

to metamodel elements (types)

precondition lhs(Par, Child, P1ClsE,ParR,AncR)

Par:entityP1:relation

Child:entity

gtrule parentIsAncR(Par,Child,ClsE,ParR,AncR)

ClsE:entityAncR:relation

ParR:relation

postcondition rhs(Par, Child, P1 ClsE,ParR,AncR)

Par:entityP1:relation

Child:entity

ClsE:entityAncR:relation

ParR:relation

E:relation

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Meta-transformations in VIATRA Main ideas:

o Transformations stored as models

o Processed by an interpreter (GT + ASM)

o Executed over models stored in the model space

Unusual consequences:o Self-modifying meta-

transformationso Pattern elements appearing

in match setsScopes of MT rules requiredEMF transformation model

Quiz: Sample model spaceo Graph pattern: chase

o Instance model

How many matches does pattern chase have in the model space? 6

Higher-Order Transformation Approaches in Tools: An Outlook

With direct contributions from M. Tisi, C. Krause, E. Syriani, H. Vangheluwe, D. Kolovos

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Our Influencers (in 2004)

•Introduced for the definition of context-sensitive language grammars

Two-level graph

grammars

•Meta-theoretical foundations in Maude

•Arbitrary models can be transformed into their meta-representation

Rewriting logics

•Reflective OO programming (Smalltalk)

•Functional programming

Programming languages

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Categorization of HOTsMT analysis• Process transformations to

extract meaningful data

MT synthesis• Create new transformations from

data modeled in other forms

MT modification• Manipulate the logic of an input

transformation

MT (de)composition• Merge /split other MTs according

to a (de)composition criterion

M. Tisi, F. Jouault, P. Fraternali, S. Ceri, and J. Bézivin. On the Use of Higher-Order Model Transformations. In Proc. of the 5thEuropean Conference on Model-Driven Architecture Foundations and Applications (ECMDA2009), pp 18–33. Springer, 2009

33

ATLTransformation

injection

• Textual transformation is translated into a transformation model

Higher-order transformation

• Transformation models for

• Input+output+HOT

Transformation extraction

• Output model is serialized into a textual transformation

34

Henshin Generic, dynamically typed MT

o Transform metamodels and instance models at the same time

o Use special wrapper objects Meta/Higher-order MT

o Henshin allows to mixnormal and HO transformations

o E.g. Ecore2GenModel(customization)

C. Krause, J. Dyck, H. Giese: Metamodel-Specific Coupled Evolution Based on Dynamically Typed Graph Transformations. In Proc. of the 6th Int. Conf. on Theory and Practice of Model Transformations (ICMT2013), pp 79–91. Springer, 2013

https://www.eclipse.org/henshin/examples.php

35

Transformation models in Ongoing work on

o parsing Epsilon programs o into abstract syntax models o that conform to Ecore metamodelso All task-specific language metamodels extend the core

(EOL) language metamodel Abstract syntax models used to perform

o static type checking, o dependency analysis, o sub-optimal code detection

Ran Wei, Dimitris S. Kolovos: Automated Analysis, Validation and Suboptimal Code Detection in Model Management Programs. In Proc. of the 2nd BigMDE Workshop, York, UK. 2014

36

Ramification in TCore Goal: Customize generic

pattern languages to domain-specific use o Generate modeling

environment quasi-automatically

o Implemented by HOTo Relax – Augment - Modify

Usage: embedding MTs into COTS toolso MetaEdit+o Matlab Simulink

Kühne, T.; Mezei, G.; Syriani, E.; Vangheluwe, H. & Wimmer, M. Systematic Transformation Development. Journal of the ECEASST: 21, Multi-Paradigm Modeling, Denver (2009).

Input Meta-Model Output Meta-Model

Relax Augment Modify

Customized Pattern Meta-Model

37

MOFLON Generic rewrite rules

o via JMI model repositoryo Example: Generic checks

• attribute to be checked passed as argument

• names of classes to be checked

Reflective rewrite ruleso Examine metamodel first

and extract propertieso Instantiate generic rules on

the instance level Application:

o Checking modeling guidelines for Simulink

Carsten Amelunxen, Elodie Legros, Andy Schürr: Generic and reflective graph transformations for the checking and enforcement of modeling guidelines. VL/HCC 2008: pp. 211-218

38

Further approaches for HOTMOTMOT (2006- ) Add language constructs

to Story Diagrams Main Usage Scenario

o Copying + Adaptationo Business Process Modeling

Medini QVT Generate copy rules from

Ecore metamodels Refinement

transformationso Generic copy ruleso Exception rules

Pieter Van Gorp, H. Schippers, D. JanssensCopying Subgraphs within Model Repositories. 5th Int. Workshop on Graph Transformation and Visual Modeling Techniques, ENTCS, pp. 127-139, Vienna, Austria, 1 April 2006. Elsevier.

Thomas Goldschmidt, Guido Wachsmuth: Refinement Transformation Support for QVT Relational Transformations. 3rd Workshop on Model Driven Software Engineering (MDSE 2008)

VIATRA Retrospective

40

Evolution of the VIATRA Tool Family

VIATRA VIATRA2

VIATRA2 R3.2 „VIATRA3”

IncQuery

EMF-IncQuery

IncQuery-D

http://www.eclipse.org/incquery/

http://www.eclipse.org/viatra2/

http://viatra.inf.mit.bme.hu/incquery






41

VIATRA Main facts & features:

o Written in Prologo XMI import/export for arbitrary

metamodelso UML as rule languageo Auto-generated Prolog code

Main applications:o SC2Promela: model checking UML

statechartso UML2DF: fault propagation

analysiso SC2SPN: reliability analysis

Major projects:o HIDE + National

Main publications: o ASE 2002, SCP 2002

•XMI export/import

•Metamodel

Start: Sep 2000

•Auto-generated MT code from UML rules

•First complex MT

M1: Summer

2001

•UML2DF transformation

•SC2SPN transformation

M2: Summer

2002

•Development of new Java/Eclipse version

End: Early 2004

42

VIATRA2 Main Features

o Unified storage of • Models + Metamodels + Transformations

o Language:• Graph transformation (GT)• Abstract state machines (ASM)

o Hybrid pattern matching• Local search + Incremental

o Live MT• Event-driven

o VIATRA DSM supporto Design-space exploration

Main Projects:o DECOS, SENSORIA, DIANA, MOGENTES

Main Publications:o SCP 2007, ICMT 08-09, MODELS09,

ASE2011, 3xSOSYM, STTT, AUSE

• Initial prototype: Spring 2004

• Eclipse start: @ GMT Sept 1st, 2005

Initial prototype: Summer 2004

•Model space + GT + ASM

•1 PhD + 3 MSc students

R1: Oct 21, 2005

•GUI improvements & documentation

•2 PhD student + 8 MSc students

R2: Oct. 10, 2006

•EMF-based MT model

•3 PhD students + 1 developer + *MSc

R3: June 2, 2008

•Incremental & live MT, MT workflow

•5 PhD students + * MSc

M3.1: March 23, 2010

•EMF support + Visualization

•UML2, BPMN, BPEL importers

M3.2: April 11, 2011

•Design-space exploration

•Incremental transitive closure

M3.3: August, 2013

•Event-driven execution platform

•Complex Event Processing

VIATRA3

Video: http://vimeo.com/7412871

43

Selected Applications of VIATRA2

Toolchain for critical embedded systems• Model-based tool integration • Workflow-driven interactive design tools• Ontology based consistency checks• Model-driven test generation

Service-oriented applications• Formal analysis and back-annotation of BPEL processes • Model based performability analysis• Model-based service deployment• Incremental generation of service configurations

GRaTS • with R. Heckel (Leicester) and P. Torrini• Stochastic simulator for graph transformation systems

44

EMF-IncQuery Main features:

o Define model queries declaratively (with Xtext editors)

o Execute queries efficiently and incrementally for EMF models

o Integrate queries into EMF apps with no coding (validations, viewers, derived features)

Contributorso BMEo IncQuery Labs (Support)o Itemis

Main publications:o MODELS10, ICMT11, MODELS12,

ECMFA12, SOSYM14, SCP2014o Demos at MODELS, ASE, ECMFA,

TOOLS, EclipseCon, …

•Initial version detached from VIATRA

•First performance benchmark

•1.5 million model elements

1st internal release: Spring

2010

•Project lead: István Ráth

•Team: Z. Ujhely, Á. Horváth, Á. Hegedüs, G. Bergmann, T. Szabó, (D. Varró)

Eclipse start: April 4, 2011

•IncQuery base: Efficient EMF navigation

•Inverse, allInstances, trans. closure

•Incremental

V0.4.0:Nov 2011

•Xtext based query editor

•Derived features

•Validation framework

V0.6.5:Oct 2012

•Event-driven VM

•Core viewers

•Xbase

V0.7.0: July, 2013

•Query libraries + Xcore

•IQ Viewers

•IQ Debugger

V0.8.0: July, 2014CERTIMO

T

Selected Applications (EMF-IncQuery)• Complex traceability• Query driven views• Abstract models by

derived objects

Toolchain for IMA configs

• Connect to Matlab Simulink model

• Export: Matlab2EMF• Change model in EMF• Re-import:

EMF2Matlab

MATLAB-EMF Bridge

• Live models (refreshed 25 frame/s)

• Complex event processing

Gesture recognition

• Experiments on open source Java projects

• Local search vs. Incremental vs. Native Java code

Detection of bad code smells

• Rules for operations• Complex structural

constraints (as GP)• Hints and guidance• Potentially infinite

state space

Design Space Exploration

• Itemis (developer)• Embraer• Thales• ThyssenKrupp• CERN

Known Users

46

IncQuery-D Main features:

o Distributed• Storages• Indexers• Incremental Queries

o Deployed over cloud infrastructure

o Goal: Scalability along• # of machines• Memory/CPU• #of concurrent users

Contributorso BME + IncQuery Labs

Main publications:o Friday@MODELS 2014o Invited talks at CloudMDE14, GT-

VMT14

•Proof of concept

•Initial architecture

•First performance benchmark

V0.1 April 2013

•Multiple storage back-ends

•Manual performance optimizations

V0.2 December

2013

•RDF-IncQuery language

•Monitoring + Optimized allocation

•First external users (students)

V0.3September

2014

47

Current Contributorso István Ráth, PhDo Ákos Horváth, PhDo Gábor Bergmann, PhDo Ábel Hegedüs, PhD candidate, (IncQuery

Labs)o Zoltán Ujhelyi, PhD candidateo Dénes Harmath (IncQuery Labs)o Tamás Szabó (Itemis)o Benedek Izsóo Gábor Szárnyaso Oszkár Semerátho Csaba Debreceni o István Dávid (Uni-Antwerp)o András Szabolcs Nagyo Márton Búro József Makaio Dániel Stein

Past Contributorso Prof. András Patariczao Gergely Varró, PhD

(TU Darmstadt)o András Balogh, PhD

(ThyssenKruppPresta)o András Schmidt (QGears)o Dávid Vágó (Google)o Balázs Grill (ThyssenKruppPresta)o Zoltán Balogho András Ökröso Balázs Polgár, PhDo Márk Czottero Dániel Tóth (NSN)o Péter Pásztoro Gergely Nyilas (Lufthansa Systems)o Zsolt Sándor (Siemens PSE)

Acknowledgements

http://www.eclipse.org/incquery/http://www.eclipse.org/viatra2/







Summary

Metamodel(Graphs)

Graph model


Meta rules

XMI document



Meta/Higher-Order Transformations

Metamodel(Graphs)

Graph model

Generic Trans. Eng.

Generic rules

XMI document

Generic Transformations

generic and meta-transformations for model transformation engineering

Documents

graph transformation

metatransformations

technological space

standard model format

qvt examplec

papernovel mt concepts

information systemsslides

pstplatform indep