Henshin: A Model Transformation Language and its Use for ... Part 2.pdf · Case 2: SCRUM Planning...

Henshin:A Model Transformation Languageand its Use for Search-Based Model

Optimisation in MDEOptimiser

Daniel Strüber1, Alexandru Burdusel2, Stefan John3, Steffen Zschaler2

1 Universität Koblenz-Landau,2 King’s College London, 3 Philipps-Universität Marburg

Fachtagung ModellierungFebruary 21, 2018

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Part 2

Overview

Part 1: Henshin: A Guided Tour Language In Action (interactive) Features Applications

Part 2: Henshin in Search-Based Model Optimization Background MDEOptimiser In Action

Case 1: Class Responsibility Assignment (interactive) Case 2: SCRUM Planning (interactive)

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Optimization problems in software engineering

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Componentdeployment

Architecturerefactoring

Sprintplanning


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Componentdeployment


Sprintplanning

Common task: find an optimal solutionamong a vast number of candidates


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Componentdeployment


Sprintplanning

Solutions

Optimality


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Componentdeployment


Sprintplanning

Solutions

Optimality

AssignmentClasses→ Packages


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Componentdeployment


Sprintplanning

Solutions

Optimality


max. Cohesionmin. Coupling


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Componentdeployment


Sprintplanning

Solutions

Optimality


AssignmentWork Items→

Sprints



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Componentdeployment


Sprintplanning

Solutions

Optimality



Sprints


max. Items/Sprint max. Customer

Satisfaction


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Componentdeployment


Sprintplanning

Solutions

Optimality



Sprints

AssignmentComponents→

Hosts


max. Items/Sprintmax. Customer

Satisfaction


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Componentdeployment


Sprintplanning

Solutions

Optimality



Sprints

AssignmentComponents→

Hosts


max. Items/Sprintmax. Customer

Satisfaction

min. Pricemin. Overhead

Example: Class Responsibility Assignment (CRA)

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Courtesy of Flecket al. [TTC 2016]


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Input model

Courtesy of Fleck et al. [TTC 2016]

Task: Assign methods+attributesto classes


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Input model Example solution




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Quality




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Quality




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Quality



Objective combining cohesion + coupling

Example: Class Responsibility Assignment (CRA)Fitness function

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Example: Class Responsibility Assignment (CRA)Fitness function

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Problem: Search space usually too large to enumerate all solutions Solution: Guided search can explore space more efficiently than humans

Mutation Crossover Selection

Fitnesscriteria

based on

Genetic algorithm

Search-based software engineering

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Problem: Search space usually too large to enumerate all solutions Solution: Guided search can explore space more efficiently than humans

Mutation Crossover Selection

Fitnesscriteria

based on

Genetic algorithm

Cost: Search algorithms need to be customized to problem at hand; substantial expertise required

Search-based software engineering

Optimalsolutionmodels

Solution: Search-based model optimisation Use models to describe solutions Standard manipulations available (model transformations!) Move optimisation knowledge from humans to tools

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Domain expert

Problemspecification


has domainknowledge

encodes optimisationknowledge

Optimization tooluses

transformationtechnology

Search-based model optimisation: what‘s needed?

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Domain expert

Solutionmeta-model

Constraints

Fitness functions

1. What dosolutions look

like?

2. Whatmakes a good

solution?3. How cansolutions be

derived?

Optimization tool


has domainknowledge

encodes optimisationknowledge

uses modelingtechnology

Overview




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MDE Optimiser Multi-objective optimization Directly over models (no separate solution encoding) Specification language + kernel Uses Henshin to specify evolutionary operators

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MDE Optimiser

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Domain expert

Solutionmeta-model

Constraints

Fitness functions

Domainknowledge

MOEAFramework

MDEOptimiser


.moptfile

Mutation operator

Henshin

Cross-overoperator

Problem specification in MDEOptimiser: CRA

Pre-defined mutation rules for CRA

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Pre-defined objective function for CRA

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Pre-defined constraint for CRA

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Pre-defined constraint for CRA

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Solution‘s „fitness“ w.r.t. a constraint:How far is the solution away from fulfilling the constraint?


„Depth“ of the search,runtime vs. effectivenesstrade-off

Overview




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MDEOptimiser in action: CRA case

1. Import projects

2. View the specification of CRA case

3. Set up and apply run configuration

4. View created results

5. Design a good mutation operator

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1. Import projects





In Eclipse, do File Import… General Existing Projects Into Workspace Next Do Select Archive File Choose optimization-cases.zip By now, you should be an expert on importing projects. :-)

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Import projects

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1. Import projects





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View the specification of CRA case

In the Package Explorer, navigate to project uk.ac.kcl.mdeoptimise.cra.solutions, folder src/models.cra

Have a look at the files:

MDEOptimiser spec: cra-solution.moptmeta-model: architectureCRA.ecorefive input models: TTC_InputRDG_<A-E>.xmiobjective function: MaximiseCRA.xtendconstraint: NoClasslessFeatures.xtendmutation operators: craEvolvers.henshin_diagram

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1. Import projects





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Set up and apply run configurationTo execute cra-solution.mopt, create a new run configuration: Click on the triangle next to the Run Icon,

select Run Configurations… Right click on MDEOptimiser Search,

select New. As Name for theconfiguration, enter: CRA Do Browse Workspace ->

Select Source cra-solution.mopt In the Classpath tab, add the

current project as a User Entry, using Add Projects… Hit Apply and Run. If everything

works correctly, you will seeconsole output like on the right.

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1. Import projects





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View created results

After approximately 15 seconds, therun is finished: No new console outputs,„Terminate“ switch has turned gray.

Results are in a newly created folder:In the Package Explorer, click on mde-results,hit F5 (refresh) and find the folder.

Open overall-results.txt. This shows theexecution time and an overview of thebest solution with its CRA index.

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1. Import projects





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Design a good mutation operator

Problem: Search is time-consuming.Solution: Improve the search by designing good evolutionaryoperators.

A mutation operator m1 is better than a mutationoperator m2, if the solutions found using m1 are betterthan those found using m2 (assuming an otherwiseequal configuration).

Task: Design a better mutation operator for the CRA casethan the given one.

Reference values:

Hint: What are desirable structures from the perspectiveof the objective function, and how to create them?

Model A B C D E

CRA 1.6 2.2 1.8 2.4 -11.6

Overview




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SCRUM Planning

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Have a backlog of workitems for differentcustomers.Need to allocate workitems to sprints.

Goals: Be fast andkeep customers satisfied.Multi-objective optimisation

problem: pareto front instead of one best solution

SCRUM Planning

Henshin - Gabriele Taentzer 94

Meta-modelStakeholder 1

Backlog

Item AImp: 3Eff: 10

Item DImp: 5Eff: 30

Item BImp: 3Eff: 10

Item EImp: 3Eff: 15

Item CImp: 3Eff: 10

Item FImp: 1Eff: 15

Example model(without sprints)

Stakeholder 2

SCRUM Planning: Objectives and constraints

Henshin - Gabriele Taentzer 95

Backlog

Example model(without sprints)

Constraint 1: Numberof sprints is below a given maximum

Constraint 2: Every workitem is assigned to a sprint.

Objective 2: Maximizecustomer satisfaction -> next slide

Objective 1: Sprints areas balanced as possible.Stakeholder 1

Stakeholder 2

Item AImp: 3Eff: 10

Item DImp: 5Eff: 30

Item BImp: 3Eff: 10

Item EImp: 3Eff: 15

Item CImp: 3Eff: 10

Item FImp: 1Eff: 15

SCRUM Planning: Customer satisfaction index

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Low means good:Indicates low standard deviation

Sprint 1 Capacity: 28 Sprint 2 Capacity: 27


SCRUM Planning: Customer satisfaction index

97𝑐𝑐𝑐𝑐𝑐𝑐 𝑝𝑝 = 𝑐𝑐𝑠𝑠𝑠𝑠.𝑠𝑠𝑑𝑑𝑣𝑣𝑐𝑐∈𝑝𝑝.𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐(𝑐𝑐𝑠𝑠𝑠𝑠.𝑠𝑠𝑑𝑑𝑣𝑣𝑐𝑐∈𝑝𝑝.𝑐𝑐𝑝𝑝𝑐𝑐𝑠𝑠𝑠𝑠𝑐𝑐𝑐𝑐 (𝑐𝑐𝑠𝑠𝑠𝑠(𝑐𝑐, 𝑐𝑐)))

𝑐𝑐𝑠𝑠𝑠𝑠 𝑐𝑐, 𝑐𝑐 = ∑𝑠𝑠∈𝑐𝑐.𝑠𝑠𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐,𝑠𝑠.𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐=𝑐𝑐 𝑐𝑐. 𝑐𝑐𝑖𝑖𝑝𝑝𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑖𝑖𝑐𝑐𝑑𝑑Satisfaction of customer c in sprint s

Customer satisfaction index of plan p


Problem specification in MDEOptimiser: SCRUM

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MDEOptimiser in action: SCRUM case

1. View the specification of SCRUM case



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View the specification of the SCRUM case

In the Package Explorer, navigate to project uk.ac.kcl.mdeoptimise.scrum.planning.solutions, folder src/models.scrum

Have a look at the files:

MDEOptimiser spec: scrum-planning.moptmeta-model: planning.ecoreinput models: input directoryobjective functions: MinimiseSprintEffortDeviation.xtend

and MinimiseCustomerSatisfactionIndex.xtendconstraints: HasTheAllowedMaximalNumberOfSprints.xtend

and HasNoUnassignedWorkItems.xtendmutation operators: sprint-repair.henshin_diagram

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Set up and apply run configuration

Similar to the CRA case, create a new configurationName: SCRUMSource: scrum-planning.moptMake sure to add the corresponding classpath entry:

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View created results

Results are, again, in a newly created folder:In the Package Explorer, click on mde-results,hit F5 (refresh) and find the folder.

Open overall-results.txt. This shows theexecution time and an overview of thebest solutions with their objective values.

Since we have multiple solutions ingeneral, an image file batch-0-pareto-front.jpeg is generatedshowing the pareto front.

Summary of Part 2

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Further information:www.eclipse.org/henshinmde-optimiser.github.io

Henshin: A Model Transformation Language and its Use for ... Part 2.pdf · Case 2: SCRUM Planning...

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