1. Introducing
Model Driven
Software Development
Pedro J. Molina, PhD.
Software Engineer
Capgemini Spain | Valencia
pjmolina@gmail.com
http://pjmolina.com/metalevel

2. Contents
 Objective
 What’s MDSD?
 Why are we doing it?
 How we can apply it?
 CG2011 Program review
2

3. Setting expectations
 What to expect from CG2011?
 The Europe leading conf. about Code
Generation
 Focused on practical tools and industrial
experiences
 A place to find and discuss with the Tool
Makers
 Probably one of the best places to learn
and share knowledge about Code
Generation
 My objective with this session
 Provide an overview to Code Generation
 Provide clues to newcomers to select
your own path in the conference
3

4. Motivation: Programmers needed!
 The impossible equation (Jean Bézivin)
1. Slow increase of professionals
 Same trend in next 25 years
2. Rapid increase of SW to be developped
3. No way to cope with it
4. End User Programming
 Example: Visical, Excel
 By DSL, abstraction
 Custom languages
http://mymodelingblog.blogspot.com/2009/08/impossible-equation.html
4

5. Abstraction Levels
The entire history of software engineering is
Application one of rising levels of abstraction (abstraction
Domain is the primary way we as humans deal with
complexity).
Grady Booch
Models / Specs
Semantic Gap
4GL
Abstraction level
COBOL / C / Basic / Java
Assembler
Machine Code
5

6. What’s a Domain?
Real Time Management
Systems Systems
Air Traffic Luggage
Insurance
Control Tracking
Systems
Systems Systems
 Focused on strict time  Focused on structure, data
constrains and tracking and transactions
6

7. What’s a Language?
Declarative
C(x) h2 2C(x)
ih = –
t 2m  x2
a>b && c==d John cookies 24 green
Phil fish 32 blue
<CallRecord>
<caller><number>07713248</number>
Textual Employee
Name
Position
Description Graphical
Address 0..*
Salary
Promote Assign
class Invoice: Document Record Cost min.
{ 
public void Bill() Duration
Imperative Call DB
7

8. What’s a model?
 A model allows
 the description of a family of problems for a domain
 Having the abstraction level carefully selected to:
 Discard irrelevant details (reduce complexity)
Source
 Discard constant details (reduce complexity)
 Explicit important details (variables part) Class Relation
Properties Cardinality
 What’s a meta-model? Target
 A model describing model.
Customer Credit Card
1 *
8

9. Some Acronyms
 MD* (MDD = MDE = MDSD  MDA) MDSD
 MDSD = Model-Driven Software
Development
MDA DSLs
 MDD = Model Driven Development
 MDE = Model Driven Engineering
 MDA = Model Driven Architecture
 OMG = Object Management Group
 DSL = Domain Specific Language
 AST = Abstract Syntactic Tree
9

10. MDD
 Model Driven Development
Definition:
The usage of Models as the main artefacts
to Drive the software Development.
10

11. Code Generation
Model Code Assets
Generator
Definition:
The automated synthesis of SW assets like
source code, documentation or models using
models as input.
11

12. Novak’s rule
“Automatic Programming is defined as the
synthesis of a program from an specification.
If automatic programming is to be useful, the
specification must be smaller and easier to
write than the program would be if written in
a conventional programming language.”
G.S. Novak
12

13. Commonality / Variability
 Family of programs
(D. Parnas)
 Common part
 Standard, Fixed.
 Implementable in common & shared base libraries
 Variable part
 Specify in the model
 Generable
13

14. Separation of Concerns (SoC)
Know-How captured in two separated buckets:
What How
 Business Know-How:  Technological Know-How:
captured in form of models captured & encapsulated in form of
(specifications): isolated from best practices, frameworks,
technological issues templates & code patterns in code
generators & interpreters.
14

15. ROI
 Economies of Scale
 Economies of Scope
 Economics of MDSD
 Development Life Cycle Impact
 Quality
15

16. Economies of Scale
 Economies of Scale
 The condition where few inputs, as effort and time, are needed to
produce big quantities of a unique output. [Wit96]
But: Can’t be applied to SW!
Once the SW is produced
Copy cost is = 0 £!
Japanese Cookie Factory. Production Line.
16

17. 17

18. Economies of Scope
 Economies of Scope
 The condition where few inputs, as effort and time, are
needed to produce a great variety of outputs. It is
produced more added value producing in the same line
different outputs. To produce each output independently
creates an overcost in the common parts.
 Economy of Scope occurs when the cost of combining two
or more products in a unique product line is lower than
producing them independently. [Wit96]
18

19. MDSD: Economic Model
Investment
Domain Engineering
Feedback:
 Clients suggestions
Application Development  Improvements for the
Environment development environment
Application Engineering
ROI (development cost saving)
Applications
19

20. MDSD: Economic Model
 Traditional Cost = N * CT
 MDD cost = Inv + N * CF
Saving AF = CT - CF
5 CT
4 CT
Accumulated costs
3 CT
2 CT
Inv
CT
1 2 3 4 5
Family members
20

21. SW Life-cycle Impact
 More time in analysis and
design tasks
 Less time in coding
 Less defect, more Quality
 Improved productivity
 Order of magnitude
 Continuous Integration
 Agile development cycles
 Less cost
21

22. Defects Cost and Distribution
Traditional life cycle
MDD life cycle Exponential cost
% Defects 8€
of defects
4€
2€
1€
Analysis Design Coding Maintenance
Snow ball effect
22

23. Models: some Cases of Use
1. Validation 2. Interpretation 3. Code Generation
Model Model Model
Model Code
Interpreter
Checker Generation
Runtime Code
interpretation
Metadata
Generated Doc
Report
Code Config.
Tests
...
23

24. Conceptual Map for Code
Generation
Types
Metamodel Templates
Transformations
Code Generator
Instances
Model Code
Higher Abstraction Level Lower
24

25. UML/MDA
 UML: Unified Modelling Language
 Origins: unified notation (from three
sources, The Three Amigos)
 OCL: constraint language
 Great acceptation, common language
for software engineering
 MDA: Model Driven Architecture
 MDA = MDSD with UML
 Proposed by OMG
 Profiles
 PIM/PSM
 MOF/XMI
25

26. Code Generation with UML tools
Types
Metamodel Templates
Transformations
Instances
Model Code
Higher Abstraction Level Lower
26

27. Code Generation with UML/MDA
 Usage of UML Models
 Extension mechanism: «persistent»
Customer
 Stereotypes
 Profiles for specialized domains «pk» string Id
«derived» double Balance
 Good enough for:
 Structure: class and properties
 State transition diagrams
(frequent in embedded systems)
27

28. Code Generation with UML/MDA
 As rich/poor enough as you can express and maintain in UML
 “Only a 20% of UML is generally needed for SW development.” Ivar Jacobson
 But: What percent of my problem can such 20% of UML solve?
 Metamodel is highly closed
 Only extensible at certain points
 Shortcomings
 Symbol and concept overloading
 Not clear Semantics
 Action Language
 Domains not cover by UML: e.g. User Interfaces
 Abuse of profiles  Forces models and semantics (One size doesn’t fit
all)
 Tools: many dialects of XMI  Babel’s Tower
28

29. DSL
 A DSL approach to MDSD gives you
full control on the development of
your metamodel
 Powerful
 Better well suited to your needs
 “Do it yourself” approach
 Tools to be built
 Metamodels, editors, model checkers,
compilers, code generators
29

30. DSL
 Internal DSL: Sample LINQ
var studentPassList = from student in students
where student.Score > 500
select student;
 External DSL: Sample SQL
SELECT Name, Surname FROM STUDENT
WHERE Score > 500;
30

31. Code Generation with EMF
Types
EMF Metamodel Templates
Transformations
Instances
EMF Model Code
Higher Abstraction Level Lower
31

32. Code Generation with MS DSL Tools
Types
DSL Metamodel T4 Templates
Transformations
Instances
DSL Model Code
Higher Abstraction Level Lower
32

33. Code generation techniques
1. Parse model Input Model
2. In-memory representation
Template
 AST / Object Model AST
3. Output building
 Free text concatenation
 XSL-T (ouch!)
 Text Template based
Output code
33

34. Tools matters
Metamodel(s)
Reverse
Textual Model Editor
Engineering
Tools
Visual Model Editor
Model(s)
Model Checker
IDE
Model Transformation
M2M M2T
Code
Model
Doc, etc.
34

35. Who are working in the area?
 Eclipse EMF/GMF  Microsoft
 IBM  DSL Tools
 SAP  MetaCase
 Obeo  MetaEdit+
 Xtext  JetBrains
 Itemis  MPS
 Intentional Software
 xUML / MDA  Intentional Workbench
 Kennedy Carter
 Blue Age  WebRatio
 Artisan  Mendix
 AndroMDA  Many more
 Olivanova  …
Presenting at CG2011 or LWC2011
35

36. Language Workbenches
Competition 2011
Tool Presenter
MPS Markus Völter
MetaEdit+ Steven Kelly
OOMEGA Christian Merenda
Whole Platform Riccardo Solmi
Rascal Jurgen Vinju
Spoofax Lennart Kats
Intentional Mats Helander
Essential Pedro J. Molina
Obeo Designer Mariot Chauvin
Xtext Karsten Thoms
Session Chair: Angelo Hulshout
Language Workbench: http://martinfowler.com/articles/languageWorkbench.html [Fowler2005]
36

37. Programme Guide
Keynotes
 Terence Parr
 ANTLR
 StringTemplate
Thursday 26th 9:00 H
 Ed Merks
 Eclipse Modeling
Framework leader
Friday 27th 9:00 H
37

38. Programme Guide
 Experience reports
 Lesson from real MDSD applications
 Tool tutorials and Hand on Labs
 Learn to use them
 Panels and Goldfish bowl
 Promote debate and participation
about a reference topic
 Birds of a Feather
 Formation of informal interest groups
on the fly 38

39. CG2011 Tips
 Choose your path
 Parallel tracks
 Split your team
 Check for slides of missing session on the conference
web, SlideShare or InfoQ.com
 Interact!
 Tweet about it using the tag #cg2011
 Install the CG2011 iPhone App
 Introduce yourself and involve in the community
 Ask whenever something is not clear to you
 Ask for each dark acronym you find
 Do not miss the traditional and relaxing punting trip!
 Continue the discussion on the social network:
http://modeldrivensoftware.net/
39

40. Conclusions
 A change in SW development is needed
 MDSD :
 Provide leverage points to reach new levels of productivity
 Offer a clear path to make better engineering in Computer
Science
40

41. Thank you!
Questions?
@pmolinam