1.
Model-Driven Development
and Code Generation
of Software Systems
Marco Brambilla
Politecnico di Milano
marco.brambilla@polimi.it
@marcobrambi

2.
Agenda
• Research field
• Research path
• A sample of research topics
• From business processes to software applications
• Generating code for crowdsourcing
• Impact and perspectives

3.
Field
Marco Brambilla
Politecnico di Milano
@marcobrambi

4.
Context
• Improving software development and maintenance
productivity
Liberated programming
Self-healing and selfevolving programs
Modeling and Code
Generation Approaches
(MDE)
End-user Development
(EUP), Mashups, …
Agile development

5.
Model-based Engineering
• Engineering practices based on models
Represents
Model
Features:
• Mapping
• Reduction
• Pragmatism
System
Purposes:
• Descriptive
• Prescriptive
[B1] Brambilla, Cabot, Wimmer.
Model-Driven Software Engineering In Practice.
Morgan & Claypool, USA, 2012.

6.
The MD* Acronyms Map
• Model-Based Engineering (MBE)
• Model-Driven Engineering (MDE)
• Model-Driven Development (MDD)
• Model-driven Architecture (MDA)

7.
The MD* Acronyms Map
• Model-Based Engineering (MBE)
• Model-Driven Engineering (MDE)
• Model-Driven Development (MDD)
• Model-driven Architecture (MDA)

8.
The MDE Ingredients
• Models (M)
• Model Transformations (MT)

9.
MDE Basic Architecture
Application
Application domain
Modeling
language
Model
Reuse
Transformation
Meta-Level
Metamodeling
language
Abstraction (bottom-up)
Transformation
definition
Transformation
language
Construction (top-down)
Artifacts
(e.g. code)
defined using
Platform
defined by
uses

10.
MDE Basic Architecture
Application
Application domain
Modeling
language
Model
Reuse
Transformation
Meta-Level
Metamodeling
language
Abstraction (bottom-up)
Transformation
definition
Transformation
language
Construction (top-down)
Artifacts
(e.g. code)
defined using
Platform
defined by
uses

11.
Research Path
Marco Brambilla
Politecnico di Milano
@marcobrambi

12.
Supported subsystems and data sources
Research path
Crowds
Crowd control & integr.
Social BPM
Social Net.
Search Eng.
SN Integr. & Expert finding
Advanced Search UI
Structured Web Data
Ontologies
Sem. Web / LD
BPM
Processes
Web Serv.
SOA
Data intensive
Relational
Requirements
Business Design
Application Design
Code Gen.
SW Devel. Process Span

13.
Supported subsystems and data sources
Main research results
Crowds.
•
•
Reactive CrowdSourcing. ACM Conf. on World Wide Web WWW 2013
Answering search queries with CrowdSearcher. ACM Conf. on World Wide Web
WWW 2012
Social Net.
•
•
Choosing the Right Crowd: Expert Finding in Social Networks, EDBT 2013
From Requirements to Implementation of Ad-hoc Social Web Applications: an
Empirical Pattern-Based Approach. IET-Sw, 2012
Search Eng. •
•
Ontologies
/ Sem. Web
A bottom-up, knowledge-aware approach to integrating and querying web data
services. ACM Trans. on the Web (TWEB), 2013
Exploratory search framework for Web data sources. VLDB Journal, 2013
Web Serv.
Relational
(& MDE)
Model-Driven Design and Development of Semantic Web Service Applications,
ACM Trans. on Internet Technology (TOIT), 2007
•
Processes
•
Process modeling in Web applications. ACM Trans. Softw. Eng. Methodol
(TOSEM), 2006
Exception Handling in Workflow-Driven Web Applications. ACM Conf. on World
Wide Web WWW 2005
•
•
Model-driven design and deployment of service-enabled web applications. ACM
Trans. Internet Technology (TOIT), 2005
•
•
Designing Data-Intensive Web Applications, Morgan-Kaufmann, 2003
The Role of Visual Tools in a Web Application Design and Verification
Framework: A Visual Notation for LTL Formulae.Conf. Web Eng. ICWE 2005
Content search over model repositories. ACM Transactions on the Web (TWEB),
2014 in print.
•

14.
Topic 1
From Business Processes
to Software Applications
Marco Brambilla
Politecnico di Milano
@marcobrambi

15.
Addressed problem
• Communication chasm between business and IT is
one of the main weaknesses of large organizations
• Different language
• Different objectives
• Different quality metrics
• Bridging the gap between business requirements
and IT realizations (from design to deployment)
• My challenge:
• Business  business processes (BPMN)
• IT  SOA-oriented and Web-based implementation

16.
Model-driven Development Process
• Manual specification of BPMN process model
• Automatic transformation of BPMN to WebML
• Possible manual refinement of WebML models
• Automatic full code generation

17.
Model-driven Development Process
• Manual specification of BPMN process model
• Automatic transformation of BPMN to WebML
• Possible manual refinement of WebML models
• Automatic full code generation

18.
BPM  Application model transformation
Transformation rules
INPUT: BPMN model enriched with data and activity typing
•
typed activities enable reusable application models
•
data dependencies are specified at high level

19.
BPM  Application model transformation
Transformation rules
OUTPUT: fine-grained Application Model, needing few refinements by the
designer

20.
BPMN  WebML transformation
(1) One control siteview per pool: Human interaction
20

21.
BPMN  WebML transformation
(1) One control siteview per pool: Human interaction
(2)
view per pool: Choreography
21

22.
BPMN  WebML transformation
(3)
(1) One control siteview perper navigationinteraction
(2)
site view per lane: user pool: WS Choreography
serviceview pool: Human
22

23.
BPMN  WebML transformation
(4) One site view per lane: business logics of activities and gateways
23

24.
BPMN  WebML transformation
Transformation lane: business component invokes
(5)
(4)
(3) Orchestration perrulesuser pool: WS Choreography the activities
(1) One control siteviewaperper navigationinteraction and gateways
(2)
site view view: controller logics of activities
serviceview pool: Human
finer-grained Application Model, needing few refinements by the designer
•
typed activities enables reusable application models
•
data dependencies are specified at a higher level
less errors in Application Model design
Faster development
24

25.
Additional investigations
• Backward engineering (2): modularization of hypertext
pieces and reuse as activity types through catalogues
• Reverse Engineering (3): decomposition of the web
application and extraction of the BP model
• Aspect-oriented adaptation of transformation rules:
•
Higher order transformations for: security, company-specific integration, …
• Cross-model
validation &
coherence

26.
Topic 2
Crowdsourcing based
web applications
Marco Brambilla
Politecnico di Milano
@marcobrambi

27.
From BPM to Social and Crowd Performers
Closed BPM
• Schema of the process decided and deployed centrally
• Tasks defined rigidly
• Actor roles predefined and assigned to tasks
Participatory
Design
• Opens the process design to multiple actors
• Stakeholders can participate and fuse processes
• End users can define their processes
Participatory
Enactment
• Social tools integrated into the process execution
• Collateral communications allowed, like following up the status,
commenting, voting, etc.
Social/Crowd
Enactment
• Process execution open (in part) to actors that are not known a priori
• Collective (community or crowd based) execution of a task allowed.

28.
From BPM to Social and Crowd Performers
Closed BPM
• Schema of the process decided and deployed centrally
• Tasks defined rigidly
• Actor roles predefined and assigned to tasks
Participatory
Design
• Opens the process design to multiple actors
• Stakeholders can participate and fuse processes
• End users can define their processes
Participatory
Enactment
• Social tools integrated into the process execution
• Collateral communications allowed, like following up the status,
commenting, voting, etc.
Social/Crowd
Enactment
• Process execution open (in part) to actors that are not known a priori
• Collective (community or crowd based) execution of a task allowed.

29.
Crowd-based Applications
Emerging crowd-based applications:
opinion mining
localized information or expert response gathering
content annotation and enrichment
General structure of crowdsourcing:
the requestor poses some questions
a wide set of responders (typically unknown to the requestor)
are in charge of providing answers
the crowdsourcing system organizes a response collection campaign

30.
Crowd Control is tough…
Crowd engineering is complicated
Task design, planning, assignment
Workers discovery, assessment, engagement
Controlling crowdsourcing is fundamental
Cost
Time
Quality
Need for higher level abstraction and tools

31.
My take on crowdsourcing: CrowdSearcher
Crowdsearcher comprises
• a conceptual framework
• a specification paradigm and tool
• a reactive execution control environment
Supports designing, deploying, monitoring, and controlling
crowd-based systems
Design is top-down, platform-independent
Deployment turns declarative specifications into platform-specific
implementations which include social networks and crowdsourcing
platforms

32.
(simplified) example: crowdsourcing opinions
on new job position (1)

33.
(simplified) example: crowdsourcing opinions
on new job position (2)

34.
The CrowdSearcher features and architecture
Cross-platform integration: social networks and Q&A websites in addition
to crowdsourcing platforms
Crowd control: model-driven engine controls application deployment and
execution
Query
Answer
API Access
Search Execution
CrowdSearcher
Engine
SE Access
Interface
Local
Source
Access
Interface
Human
Access
Interface
Human Interaction
Management
Query Interface
Social
Networks
Q&A
Crowdsource
platforms

35.
The Design Process of crowd-based apps
within CrowdSearcher
A simple task design and deployment process, based on specific
data structures
created using model-driven transformations
driven by the task specification
Task Specification
Task Planning
Control
Specification
• Task Specification: task operations, objects, and performers
• Task Planning: work distribution
• Control Specification: task control policies

36.
Model-driven engineering setting
App.-specific
Data Schema
Task Specification
Data
Instances
Task Model
Operations
T1
Strategy
T2
T3
Input design
model
Transform.
Generated
artifact
artifact flow
App.-specific
Data Model
Task Planning
Microtask
Logic and UI
Microtasks &
Executions
T4
Control
Specification
Event-based
Control
Microtask
deployment
T5
Crowd Control
Mart
T6
Control
Rules
Crowdsourcing
Platforms /
Communities

37.
Model-driven engineering setting
App.-specific
Data Schema
Task Specification
Data
Instances
Task Model
Operations
T1
Strategy
T2
T3
Input design
model
Transform.
Generated
artifact
artifact flow
App.-specific
Data Model
Task Planning
Microtask
Logic and UI
Microtasks &
Executions
T4
Control
Specification
Event-based
Control
Microtask
deployment
T5
Crowd Control
Mart
T6
Control
Rules
Crowdsourcing
Platforms /
Communities

38.
Model-driven engineering setting
App.-specific
Data Schema
(in MetaModel)
Task Model
Input design
model
Task Specification
Operations
T1
Strategy
Transform.
T2
T3
Generated
artifact
artifact flow
App.-specific
Data Model
Microtask Impl.
Logic and UI
T4
App.-specific
Data Instances
Crowd Control
Mart
Event-based
Control
Microtask
deployment
T5
T6
Control Rules
Crowdsourcing
Platforms /
Communities
Task Planning
Control
Specification
Microtasks and
Executions

39.
CrowdSearcher Meta-Model

40.
CrowdSearcher Datamart for Runtime Control

41.
Example experiment: Professors’ images
Classification of images retrieved
from Google
• 16 professors within 2
research groups in DEIB
(DB and AI groups)
• The top 50 images returned
by the Google Image API for
each query
• Each microtask consisted of
classifying 5 images regarding
a professor
• Agreement of multiple
performers checked on the
image
• Randomization of the image
order

42.
Experiment objectives
Validating impact of model-driven change of:
• Crowdsourcing strategies (majority, agreement)
• Crowd communities
• Control rules (order of invitations, …)
Measures:
• Speed of result collection (number of closed objects in time)
• Quality of classification results (precision of responses)
The communities:
•
the research group of the professor
•
the research area containing the group (e.g. Computer Science)
•
and the whole department (more than 600 people in different areas)
•
Polimi-related social networks

43.
Precision of communities
1"
0.9"
0.8"
Precision)
0.7"
0.6"
0.5"
0.4"
research"group"
0.3"
research"area"
department"
0.2"
social"network"
0.1"
total"
0"
0"
500"
1000"
1500"
#Evalua0ons)
2000"
2500"
3000"

44.
Control rules: order of invitation
Precision decreases with less expert communities
Outside-in strategy (from generic to expert users) outperforms
inside-out strategy (from expert to generic)
1$
precision$
(main$
experiment)$
precision$
(reverse$
invita<ons)$
Precision)
0.95$
0.9$
0.85$
0.8$
0.75$
0.7$
0.65$
0.6$
0$
100$
200$
300$
400$
#Closed)Objects)
500$
600$
700$
800$

45.
% of Compl. Objects
Precision
Strategies Evaluation: different majorities
Majority @7
Early Majority @3 R @7
Majority @3 R @5 R @7
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
+26% precision
+50% precision
-23% executions
0
10
20
30
40
50
#Executions
60
70
80
90

46.
Evaluations
• Different strategies and communities impact on quality, cost
and time
• Cost of adapting is very low thanks to definition of strategies
and rules at design level and model-driven generation of
running system

47.
Impact
Marco Brambilla
Politecnico di Milano
@marcobrambi

48.
Standardization
User interaction has been overlooked in software engineering standards
Hence the Interaction Flow Modeling Language (IFML)
Proposed as a standard in
OMG (Object Management
Group)
In less than 2 years (a record
in OMG!), we obtained
approval of the IFML
standard (March 2013)
Final voting on version 1.0, after 3+ years of work
Brambilla, Fraternali. “Model-driven UI
Engineering of Web and Mobile Apps with IFML”.
Morgan Kauffmann, USA, 2014.

49.
Industrial exploitation
IFML (and formerly WebML)
has been exploited by the
spinoff WebRatio
Commercial modeler and code generator
based on IFML and BPMN
17,000 academic licenses
150+ industrial customers (including Acer, Ikea, Unicredit, General
Electrics, …)
US and LA branches
Different time-to-market for research ideas
•
From 0 (web service modeling) to 4 (BPM) years!

50.
Conclusion: innovation-enabling ecosystem
innovation
Research
(Research group
at Polimi)
Spin-off
(WebRatio)
Toolsuite, industrial requirements
innovation
innovation
Teaching
(Polimi and int.l courses)
Method,
Language
and Platform
& other SME
Partners
toolsuite
Standard
Customers
use cases
(including EU
projects)
requirements

51.
Thanks
Marco Brambilla
Politecnico di Milano
@marcobrambi