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Implementing Software Product Line Adoption

StrategiesCandidate:Vander Alves

Supervisors: Paulo Borba and Geber RamalhoMarch 23, 2007

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Different devices, 15 to 60 different applications…

With plenty of common

functionality, but also with

variability

64kb, flip 4Mb, flip 100Kb, no flip

3

A

CB'

D

Little reuse and high cost

A

CB

A

CB

D

4

Goal: automate product line

A

CB'

DA

CB

A

CB

D

A

B

C

D B'

DStrategic reuse of common artifacts and handling of

variability

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Software Product Lines (SPL)

Source: SEI

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GO SPL!SPL benefits• Higher productivity• Higher quality• Low cost

But higher complexity• Variability management• Adoption strategies

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SPL challengesVariability managementAdoption strategy

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Variability management

Highly variant domains• Realm of SPL• Different artifacts• Often crosscutting implementation• high instantiation cost

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Mobile games domainScreen SizesKeyboard Specific APIs

MemoryProcessing PowerNumber of colors

Porting Porting

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Massive variability

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Aspect-Oriented Programming

Extension to OOModularizing crosscutting concerns• Tangling• ScatteringLanguage and tool supportDifferent levels of abstraction (AOSD, Early Aspects,…)

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Adoption StrategiesProactiveExtractiveReactive

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Source: Krueger, PFE’01

Proactive Adoption Strategy

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Source: Krueger, PFE’01

Reactive Adoption Strategy

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Source: Krueger, PFE’01

Extractive Adoption Strategy

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ProblemReactive and Extractive very relevantLack of guidelines• Implementation• Feature modelEssential requirements• Concrete guidelines• Safety

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Key ideaMethod to create and evolve product lines• Combine adoption strategies • Refactorings to guide the process

Concrete guidelinesSafety

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IdentifyVariability

ExtractSPL

ReactSPL

MigrateSPL

RefactorFeature Model

UpdateConfiguration Knowledge

Our Method

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IdentifyVariability

ExtractSPL

ReactSPL

MigrateSPL

RefactorFeature Model

UpdateConfiguration Knowledge

Our Method

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Extract SPL

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IdentifyVariability

ExtractSPL

ReactSPL

MigrateSPL

RefactorFeature Model

UpdateConfiguration Knowledge

Our Method

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React SPL

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Further React SPL

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IdentifyVariability

ExtractSPL

ReactSPL

MigrateSPL

RefactorFeature Model

UpdateConfiguration Knowledge

Our Method

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Migrate SPL

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Method propertiesExtractive + reactive approachesAOP-based variation management• isolate crosscutting variations from the

core• reuse opportunityRefactorings to guide the process• Concrete and declarative guidelines • Safety• Derived from elementary AOP laws

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Example - extractive refactoring

Load images variation• Images are loaded on demand• Images loaded only once

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Extractive RefactoringLoad images variation point

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Extractive Refactoring example

tsclass C {

fsmsT' m(ps) {

body'body

}}

tsclass C {

fsmsT' m(ps) {

body}

}privileged aspect A {before (context):

execution (σ(C.m)) && bind (context) {

body' [cthis/this]}

}

•body does not use local variables declared in body‘;•there is no designator within or withincode capturing join points inside body‘; …

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Refactorings

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IdentifyVariability

ExtractSPL

ReactSPL

MigrateSPL

RefactorFeature Model

UpdateConfiguration Knowledge

Our Method

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Product line derivation and evolution benefit from refactoring

Core

Startup

Core’

OnDemand Hybrid OnDemand Hybrid

Product 1

Product 2

Guidelines and safety

Extractive Reactive

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But PL refactoring should go beyond code…

coreA +Startup +Clouds

coreA

Startup

Clouds

Startup Clouds Startup Clouds

Game GameRain of fire

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Startup

Clouds

coreB

coreBStartup

Clouds

coreB

OnDemand Hybrid

OnDemand Hybrid

and deal with populations and families too!

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Feature model refactorings as improved configurability

A

B C

A

B C⊑

{A,B} ⊆ {A,B}

{A,C} {A,C}

… {A,B,C}…

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Refactoring catalog

A

B

forms

C

A

BfsB

forms

CfsC

⊑

A

B

forms

⊑A

B

forms

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Refactoring populations and families: feature models

A

B C

A

B C⊑

A

B

FM1 FM2 FM3

fm1 fm2 ⊑ fm =fm1 ⊑ fm ∧ fm2 ⊑ fm

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Case StudiesEvaluate method in industrial-strength domainConsider analytical and quantitative dataIdentify enhancements to method

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Rain of FireExtractive/Reactive5KLOC

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Rain of Fire

Variability identification with concern graphsPlatform variation fully isolated• Load images on demand• flip feature reusedRefactoring set was useful guideCoarse-grained configuration knowledge

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0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

P1 P2 P3 Total

Line

s of

Cod

e

Original Implementation

SPL Implementation

Core Assets

Classes

Aspects

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Best Lap15 KLOC16 families, 65 devicesMigrate stepVariability identification by locating tags with toolMigration strategies• Arena feature• Mostly static variability

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Configuration knowledgeVaried granularityNon-compositional piecesReflect feature dependency• Arena feature

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Aspect reusability in Best Lap

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AnalysisMinor increase on SPL LOC• Aspect declarationsBut improved modularity• Reusability• Locality • Adaptability • Pluggability• Independent development14 % increase on Jar size

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ConclusionsRelevance of extractive and reactive adoption strategiesOrthogonal relevance of variability managementsMotivation in highly variant domain

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ConclusionsMethod for SPL extraction and reactionRefactorings for variability management• Guidelines and safety• Novel notion for SPLsValidation in case studiesDomain independent method

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Assumptions and limitationsAOP does not handle all variability• But most variabilities and comparative

analysisPointcut languages still too syntactic• But tool support alleviates thisInefficient AOP implementations• But optimizers may mitigate this

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Future workTraceability with tests and architectureVariability in other artefactsMore case studiesCase studies in other domains

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Future workDefinition of metric suite for SPLExtend tool supportHandle additional variability• Import variation• Super class constructor call• Adding an else-if Block

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Implementing Software Product Line Adoption

StrategiesCandidate:Vander Alves

Supervisors: Paulo Borba and Geber RamalhoMarch 23, 2007

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Backup slides

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Tool SupportSpecially critical for PLsPrototypeAspectJEclipse (JDT & AJDT)

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Extract Before Block

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Extract Before Block

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Extract Before Block

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Extract Before Block

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