A tool demonstration of the MOMoT framework for the ICMT conference 2016 in Vienna. The demonstration shows the application of search-based model transformations on the Class Responsibility Assignment problem.

1. Search-Based Model Transformations with MOMoT
http://martin-fleck.github.io/momot/
ICMT, Vienna
4th July 2016
A Tool Demonstration
Martin Fleck, Javier Troya, Manuel Wimmer

2. Problem Statement
2
Solving Complex Optimization Problems
 Example: Class Responsibility Assignment Problem
 Modularization of features into classes

3. Problem Statement
2
Solving Complex Optimization Problems
 Example: Class Responsibility Assignment Problem
 Modularization of features into classes
1 2 5 15 52 203 …
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
1,000,000,000
10,000,000,000
100,000,000,000
1,000,000,000,000
10,000,000,000,000
100,000,000,000,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
NumberofPossibilities
Number of Features

4. Metamodel?
Problem Statement
3
Solving Complex Optimization Problems
ClassModel
name : EString
Feature
name : EString
Class
name : EString
features
classes
isEncapsulatedBy
dataDep
0..*
0..*
0..*
0..*
0..1
0..*
AttributeMethod
encapsulates
funcDep

5. Trans-
formations?
Problem Statement
3
Solving Complex Optimization Problems
Rule createClass(className: EString)
«preserve»
: ClassModel
«forbid»
: Class
name = className
«create»
: Class
name = className
«create»
classesclasses
«forbid»
Rule assignFeature(feature: Feature, class: Class) @ClassModel
«preserve»
feature : Feature
«forbid»
: Class
«preserve»
class : Class
«create»
moduleclass
«forbid»

6. Problem Statement
3
Solving Complex Optimization Problems
Coupling?
Cohesion?
?

7.  Manually solving problems intelligently requires detailed knowledge
 Effect of transformation rules on output model
 Conflicting and supporting rules
 Rule parameterization
 Rule scheduling: Order of rules
 Conflicting output characteristics
Problem Statement
3
Solving Complex Optimization Problems
Rule createClass(className: EString)
«preserve»
: ClassModel
«forbid»
: Class
name = className
«create»
: Class
name = className
«create»
classesclasses
«forbid»
Rule assignFeature(feature: Feature, class: Class) @ClassModel
«preserve»
feature : Feature
«forbid»
: Class
«preserve»
class : Class
«create»
moduleclass
«forbid»

8.  Manually solving problems intelligently requires detailed knowledge
 Effect of transformation rules on output model
 Conflicting and supporting rules
 Rule parameterization
 Rule scheduling: Order of rules
 Conflicting output characteristics
 Complex problem with large or even infinite transformation space
 Automatic transformation orchestration
Problem Statement
3
Solving Complex Optimization Problems
Rule createClass(className: EString)
«preserve»
: ClassModel
«forbid»
: Class
name = className
«create»
: Class
name = className
«create»
classesclasses
«forbid»
Rule assignFeature(feature: Feature, class: Class) @ClassModel
«preserve»
feature : Feature
«forbid»
: Class
«preserve»
class : Class
«create»
moduleclass
«forbid»

9. Search-Based Model Transformations
4
Overview
Model-Driven
Engineering
(MDE)
Search-Based
Optimization
(SBO)
Search-Based Model Transformations

10.  Aim: Automatic transformation orchestration for complex problems
 Utilize abstraction capabilities of MDE
 Benefit from declarative exploration capabilities of SBO methods
Search-Based Model Transformations
5
Overview

11.  Aim: Automatic transformation orchestration for complex problems
 Utilize abstraction capabilities of MDE
 Benefit from declarative exploration capabilities of SBO methods
 Search-Based Exploration
 Example: Population-based search method
Search-Based Model Transformations
5
Overview
Population
(size = 10)

12.  Aim: Automatic transformation orchestration for complex problems
 Utilize abstraction capabilities of MDE
 Benefit from declarative exploration capabilities of SBO methods
 Search-Based Exploration
 Example: Population-based search method
Search-Based Model Transformations
5
Overview
Select k
random
(k = 4)
= 1
= 3
= 2
= 4
Population
(size = 10)

13.  Aim: Automatic transformation orchestration for complex problems
 Utilize abstraction capabilities of MDE
 Benefit from declarative exploration capabilities of SBO methods
 Search-Based Exploration
 Example: Population-based search method
Search-Based Model Transformations
5
Overview
Select k
random
(k = 4)
Choose n
best
(n = 2)
= 1
= 3
= 2
= 4
Population
(size = 10)

14.  Aim: Automatic transformation orchestration for complex problems
 Utilize abstraction capabilities of MDE
 Benefit from declarative exploration capabilities of SBO methods
 Search-Based Exploration
 Example: Population-based search method
Search-Based Model Transformations
5
Overview
Select k
random
(k = 4)
Choose n
best
(n = 2)
Recombine
= 1
= 3
= 2
= 4
Population
(size = 10)
Cut Point

15.  Aim: Automatic transformation orchestration for complex problems
 Utilize abstraction capabilities of MDE
 Benefit from declarative exploration capabilities of SBO methods
 Search-Based Exploration
 Example: Population-based search method
Search-Based Model Transformations
5
Overview
Select k
random
(k = 4)
Choose n
best
(n = 2)
Recombine Mutate
= 1
= 3
= 2
= 4
Population
(size = 10)
Cut Point

16.  Aim: Automatic transformation orchestration for complex problems
 Utilize abstraction capabilities of MDE
 Benefit from declarative exploration capabilities of SBO methods
 Search-Based Exploration
 Example: Population-based search method
 Idea: Define generic encoding based on model transformations
 Transformation as sequence of transformation units
 Parameters set automatically and randomly
Search-Based Model Transformations
5
Overview
Select k
random
(k = 4)
Choose n
best
(n = 2)
Recombine Mutate
= 1
= 3
= 2
= 4
Population
(size = 10)
Cut Point
Transformation Units = Decision Variables
rule = createClass
name = 'Class A'
rule = assignFeature
class = Class A
feature = Feature A
Placeholder
rule = createClass
name = 'Class B'

17.  Marrying Optimization and Model Transformations
 Based on Eclipse platform
 Eclipse EMF as modeling environment
 Henshin as graph transformation engine
 MOEA Framework for SBO algorithms
Search-Based Model Transformations
6
Marrying Optimization and Model Transformations (MOMoT)
MOMoT
Problem Instance Model
Objectives
Constraints
Transformation Rules
Exploration Configuration
Result Models
Objective Values
Generic
Encoding
Search-based
Exploration
Result
Generation
Constraint Values
Rule Orchestrations
Exploration Statistics
Objective and
Constraint Evaluation
Transformation
Orchestration
[1] MOMoT Project: http://martin-fleck.github.io/momot/
[2] Eclipse Modeling Framework: https://eclipse.org/modeling/emf/
[3] Henshin Project: https://www.eclipse.org/henshin/
[4] MOEA Framework: http://moeaframework.org/
MOEA

18. Search-Based Model Transformations
7
Demo
Demo
(linked in next slide and available at
https://www.youtube.com/watch?v=BQDz6OmxABE)

19.  Summary
 Transformation orchestration is a non-trivial task
 Smart exploration of transformation space to solve complex problems
 Generic: Problem- and SBO method-agnostic approach
 Transparent: Input and output provided directly on model level
 Declarative: Objectives and constraints specified using MDE techniques
 Supportive: Dedicated configuration language for model engineers
Search-Based Model Transformations
8
Summary
MOMoT
Problem Instance Model
Objectives
Constraints
Transformation Rules
Exploration Configuration
Result Models
Objective Values
Generic
Encoding
Search-based
Exploration
Result
Generation
Constraint Values
Rule Orchestrations
Exploration Statistics
Objective and
Constraint Evaluation
Transformation
Orchestration

20.  Interesting Research Lines
 Application on out-place transformations
 Support of memetic algorithms
 Integration of optimization method languages
Search-Based Model Transformations
9
Further Research
MOMoT
Problem Instance Model
Objectives
Constraints
Transformation Rules
Exploration Configuration
Result Models
Objective Values
Generic
Encoding
Search-based
Exploration
Result
Generation
Constraint Values
Rule Orchestrations
Exploration Statistics
Objective and
Constraint Evaluation
Transformation
Orchestration

21. Search-Based Model Transformations with MOMoT
http://martin-fleck.github.io/momot/
ICMT, Vienna
4th July 2016
A Tool Demonstration
Martin Fleck, Javier Troya, Manuel Wimmer