1. Web Technologies
Introduction to
Model Driven Engineering
Piero Fraternali
piero.fraternali@polimi.it
Skype: pierofraternali

2. Models: a meeting place for
developers & users

3. Motivations
• “Supply of talented
IT staffers isn't
keeping up with #applications
demand. And it
won't change
ers
anytime soon.” ram
m
#p rog
– Eric Chabrow, in
The New IT Worker
Shortage, CIOInsight
2005 2010 2015 2020
2009

4. From crafting to fabrication
• Engineering
addresses the
optimization of
production by means
of abstraction and
standardization
• Products are
modeled, verified, and
produced using
standard models and
methods

5. Models
• A model is an
ABSTRACT
REPRESENTATION
of a product, service
or system that
allows us to
investigate, verify,
and document its
properties BEFORE
production

6. Software as an engineering product
• SW is an engineering
product
• BUT:
– It can be changed easily,
even after delivery and
during usage
– It is produced in short time
and at low cost
– It must satisfy a broad
spectrum of requisities,
hard to quantify
• SO engineering practices
are not always applied

7. Software Models
• Software Engineering has a long tradition on the
use of abstraction and software models
• MODEL DRIVEN ENGINEERING
• SOFTWARE MODELS are
– An abstract representation of a system,
independent of technology
– A tool for expressing and verifying requirements
– A starting point for automatically generating the
code

8. History
• Software models are as old
as software
• Flow diagrams were the first
tool for describing software
• Programming language and
models evolved together
– Imperative programming –
flow diagrams
– Info systems – ER
– OO systems – object models

9. Milestones
D. Harel,
"Statecharts:
A Visual
Formalism
for Complex
Systems",
(1987)
P.- Chen
The Entity-
Relationship
Model-Toward
a Unified
View of Data
TODS 1976

10. Outlook (by Gartner 2010)
• Key Findings in MDE
– The complexity of new, service-oriented
architectures (SOAs) and business
process management (BPM) demand more-
abstracted, model-driven approaches.
– Business and IT modeling standards and
technologies are converging.
– The differences between design time and
runtime technologies are disappearing

11. Modeling convergence

12. Outlook (by Gartner 2010)
• Recommendations
– Ensure collaboration among the IT organization,
business process architects and the analysts who
are using these models;
– Use multiple methodologies and approaches to
Application Development — especially those that
leverage modeling efforts, agile methods, and
reusable design patterns and frameworks to
improve productivity, while ensuring quality and
performance.
– Implement the role of application architect to
promote reusable software and data services.

13. Economic Value (BPM)

14. MDA: The Modern Age of MDE
• Object Management
Group (OMG) unified
several modeling
languages into a coherent
proposal (MDA= Model
Driven Architecture)
setting the stage for a
convergence of
architecture, languages,
notations, and tools
• UML is the most popular
ingredient of MDA but not
the only one

15. MDD, MDA, MDE
MODEL DRIVEN
COST ESTIMATION MODEL DRIVEN
ENGINEERING
MODEL DRIVEN
DEVELOPMENT
N
I VE RE
DR TU
L C
DE ITE
MO CH MODEL DRIVEN
AR TESTING
MODEL SEARCH

16. History
• 1989: OMG formed to create a standard for
distributed object systems
• 1991: Corba 1.0 specification released
• 1996: OMG includes modeling as a focus
• 1997: UML adopted by OMG
• 2001: OMG adopts MDA
• 2005: UML 2.0 adopted by OMG
• 2006: BPMN standard adopted by OMG
• 2009: BPMN 2.0 beta 1

17. OMG MDA standards
• OMG Standards
– Modeling – UML
– Metamodeling – MOF
– Action semantics
– Model interchange – XMI
– Human-usable textual notation – HUTN
– Model-based testing and debugging
– Domain-specific UML profiles

18. MDA core concepts
• System = the subject: a program, a single
computer system, some combination of parts of
different systems, a federation of systems, each
under separate control, people, an enterprise, a
federation of enterprises…
• Problem space (Domain): the context where
the system operates
• Solution Space: the spectrum of possible
solutions that satisfy the system requirements
• Model: a (partial or total, abstract or concrete)
representation of the system and its
environment

19. MDA core concepts
• Architecture = specification of the parts and
connectors of the system and the rules for the
interactions of the parts using the connectors
• Viewpoint: description of a system that focuses
on particular concerns
• View: a model of a system under a specific
viewpoint
• Transformation: the conversion of a model into
another model

20. MDA core concepts
• Platform: set of subsystems and
technologies that provide a coherent set of
functionality through interfaces and
specified usage patterns (e.g., JEE, .NET)
• Platform Model: a set of technical
concepts, representing the different parts
that make up a platform and the services
provided by that platform

21. Development
• In MDA development
is a chain of
transformations from
model to model, until
enough
“concreteness” is
incorporated to make
the model
“executable”

22. How are models specified
• M0: a concrete system, M3: meta-meta-model
your application describes
• M1: the model of your
describes
system
• M2: the concepts used to
represent your models M2: meta-model
(e.g., UML or BPMN
metamodels) describes
• M3: the formalism that
dicates the rules for M1: model
defining modeling
languages (e.g., UML describes
metamodel expressed in
Meta Objet Format-MOF) M0: system

23. Example
metamodel
customer order model
Customer_table Order_table
system

24. Why meta-models
• Allow a syntactically precise definition of modeling
languages
• Can be read by tools, enable portability, model parsing
(syntactic) checking
• Like grammars for textual languages and ontologies for
semantic languages
The WebML
metamodel
(fragment)

25. Classes of models (viewpoints)
• Computation
Independent Models: CIM
represent the business
view of the system mapping
• Platform Independent
Models: represents the PIM
system abstracting from
technology mapping
• Platform Specific
Models: represents the PSM
system considering
technology

26. CIM: examples
• Business Process
Modeling Notation:
– BPMN provides a
Business Process
Diagram (BPD), which
is a Diagram designed
for use by the people
who design and
manage business
processes.

27. PIM: examples
• Production Rule Representation
(PRR)
– a standard production rule
representation that is compatible with
rule engine vendors' definitions of
production rules
• Product Lifecycle Management
Services (PLM)
– a PIM for Product Lifecycle
Management Services derived from
the ISO 10303-214 STEP model
• Gene Expression (GENE)
– representation of gene expression data
and relevant annotations, as well as
mechanisms for exchanging these data
• General Ledger (LEDG)
– interfaces and their semantics to
enable interoperability between
General Ledger systems and
accounting applications

28. Model Transformations
• The application of Source
mapping rules and model
knowledge to a Additional
information
source model to
obtain a target model Transfor
• Can use different mation
Design
styles, based on how Patterns
the mapping rules are
specified Target
model

29. In this Course
CIM
• Business process
Modeling Notation
mapping (BPMN 2.0)
PIM • Web Modeling
mapping Language (WebML)
• Java enterprise
PSM
Edition (JEE)

30. Domain Specific Languages (DSL)
• DSL: a language for
describing systems in a
specific domain (Web
apps, embedded
systems, etc)
• General Purpose
Language: a language
for describing systems in
multiple domains (e.g.,
UML)

31. MDA and DSL
• MDA offers two
standard ways to
define DSLs
• Stereotyping: UML
2.0 elements are
overloaded with
domain specific
meaning
• Metamodelling:
new language
constructs are defined

32. GPL vs DSL
• GPL • DSL
– (Often) Standard – Easier to learn for domain
– Portable across tools experts
– More complex – More precise semantics,
– better checking
Less intuitive to learn
– More amenable to
– Lack of precise semantics
optimized code generation
– Harder to use for code
– Narrower scope
generation
– Non standard
– Less portable across tools

33. GPL: UML
• A concrete syntax for
specifying any kind of
system
• Customizable with
extension mechanism
(family of languages:
Real-Time UML, UML
for EDOC..)
• Supported by tools

34. UML structure
Complete
level
MACHINES
STATE
COMPONENTS
CLASSES &
ACTIVITIES
INTERACTIONS
ACTIONS
FLOWS
Intermediate
level
PROFILES
MOF OCL
Basic level
BASIC UML
UML INFRASTRUCTURE

35. DSL: forms

36. DSL: examples
DSL Application Domain
BNF (1950! ) Syntax Specification
HTML Hypertext Web Pages
MATLAB Technical Computing
SQL Database Queries
LaTex Text processing
Marjan Mernik, University of Maribor
Jan Heering, CWI
Anthony M. Sloane, Macquarie University
ACM Computing Surveys, Vol 37, No. 4

37. Executability of DSL
• Well-defined execution semantics
– HTML
• Input language of an application generator
– WebML: Web/SOA modeling
– Application generator compiles the DSL into a GPL (Java)
– More declarative, well-defined execution semantics
(StateCharts)
• Not executable but useful for application generation
– BNF: purely declarative, but can be an input language for a
parser generator
• DSL not meant to be executable
– Domain-specific data structure representations
– Benefit from editors, analyzers, consistency checkers

38. Language levels (Caper Jones)
Language Level Relationship to Productivity
LANGUAGE LEVEL PRODUCTIVITY AVERAGE PER STAFF MONTH
-------------------------- ------------------------------------------------------------------
1-3 5 to 10 Function Points
4-8 10 to 20 Function Points
9 - 15 16 to 23 Function Points
16 - 23 15 to 30 Function Points
24 - 55 30 to 50 Function Points
Above 55 40 to 100 Function Points EXCEL
MATHCAD

39. Visual DSLs
Solve problem in domain terms
Domain Map to code, implement Finished
Assembler
Idea Product
Map to code, implement
Code
Generate,
Add bodies
Map to UML
UML Model
No map! DSVL Generate calls
Model Components
to components
Framework for Domain-Specific Visual Languages OOPSLA workshop
Juha-Pekka Tolvanen, Steven Kelly, Jeff Gray, Kalle Lyytinen on DSVL

40. Visual DLS design and
implementation
Domain Finished
Idea Product
Expert
(few)
DSVL Code Component
metamodel generation library
Normal Easy! DSVL Generate calls
Model Components
(many) to components

41. Meta CASE for DSVL design
Rules Generators
1 2 3 4
Concepts Symbols

42. MDA and Tools
Metamodel editors
Metamodels
Textual model Editor Reverse engineering
Visual Model Editor Import & Export
MODELS
Model Checker
Teamwork
M2M M2text
OTHER SOURCE
MODELS CODE

43. References
• Peter Pin-Shan Chen "The Entity-Relationship Model: Toward a Unified
View of Data" ACM on Database Systems, Vol. 1, No. 1, March 1976
• Chen, Peter P., Entity-Relationship Modelling: Historical Events, Future
Trends, and Lessons Learned, Springer-Verlag New York, Inc, 2002, pp.
296-310
• D. Harel, "Statecharts: A Visual Formalism for Complex Systems", Sci.
Comput. Programming 8 (1987), 231-274.
• J, Rambaugh, Object-Oriented Modeling and Design. With others. Prentice
Hall, ISBN 0-13-629841-9, 1991
• G. Booch, Object-Oriented Analysis and Design with Applications. Benjamin
Cummings, 1991 (2° Edition 1994, Addison Wesley)
• I. Jacobson, Object-Oriented Software Engineering: A Use Case Driven
Approach (ACM Press) With Magnus Christerson, Patrik Jonsson & Gunnar
Overgaard. Addison-Wesley, 1992, ISBN 0-201-54435-0, 1992
• I. Jacobson, Grady Booch & James Rumbaugh, The Unified Software
Development Process. Addison-Wesley Professional, 1999, ISBN 0-201-
57169-2, 1999

44. References
• Frankel. “Model Driven Architecture: Applying MDA to
Enterprise Computing.” Wiley, 2003
• MDA Guide
– www.omg.com/mda
• Marjan Mernik, Jan Heering, Anthony M. Sloane: When
and how to develop domain-specific languages. ACM
Comput. Surv. 37(4): 316-344 (2005)
• Jeff Gray, Kathleen Fisher, Charles Consel, Gabor
Karsai, Marjan Mernik, Juha-Pekka Tolvanen: DSLs: the
good, the bad, and the ugly. OOPSLA Companion 2008:
791-794
• Juha-Pekka Tolvanen, Steven Kelly, Jeff Gray, Kalle
Lyytinen, Framework for Domain-Specific Visual
Languages, OOPSLA WS on DSVL 2001

45. References
• Wynn, Moe T. and Verbeek, H.M.W. and van der
Aalst, Wil M. and ter Hofstede, Arthur H.M. and
Edmond, David (2007) Business Process
Verification - Finally a Reality! Business Process
Management Journal.
• Johan den Haan, Model Driven Engineering,
http://www.theenterprisearchitect.eu/archive/200
9/01/15/mde---model-driven-engineering----
reference-guide
• Mike Blechar, David Norton , Trends in Model-
Driven Development, 4Q09-3Q10, Gartner