Arev05c icfca05lessonslearned

Lessons Learned in Lessons Learned in Applying Formal Concept Analysis Applying Formal Concept Analysis

to Reverse Engineeringto Reverse Engineering

Gabriela B. ArévaloGabriela B. Arévalo, St, Stéphane Ducasse, Oscar Nierstraszéphane Ducasse, Oscar NierstraszSoftware Composition GroupSoftware Composition Group

University of Bern, SwitzerlandUniversity of Bern, Switzerlandhttp://www.iam.unibe.ch/~scghttp://www.iam.unibe.ch/~scg

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University of Bern 2/19Gabriela B. Arévalo

RoadmapRoadmap

Motivation: Dependencies in Software SystemsMotivation: Dependencies in Software Systems

High-Level Views based on FCAHigh-Level Views based on FCA

Our Methodology and Issues to apply itOur Methodology and Issues to apply it

High-Level Views and IssuesHigh-Level Views and Issues

Lessons Learned and ConclusionsLessons Learned and Conclusions


Maintenance of Software SystemsMaintenance of Software Systems

Software System: Laws of Continuing Change and Increasing Complexity

Maintenance of software systems

50% to 75 % of the total cost

Reading and Analysis of Software:

½ time of maintenance phase

We need to detect Implicit Contracts between software components in a system

50% - 75 %

50%


Dependencies in Object Oriented SystemsDependencies in Object Oriented Systems

Software System seen as a puzzleSoftware System seen as a puzzle

Reuse

Data Encapsulation

Delegation

Dynamic Binding

Inheritance

Polymorphism

Appear several times

Do not appear isolated

Can appear at different abstraction levels

Dependency is the Dependency is the glueglue between the objects between the objects


High-Level Views based on Formal High-Level Views based on Formal Concept Analysis (FCA)Concept Analysis (FCA)

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Formal Concept Analysis as a

base tool

High Level Views

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Object Oriented Systems


Our MethodologyOur Methodology

<

Source Code

Magnitude Integer

Inheritance

Model Import

Source code model

Magnitude isAbstract

<self send = ….. >

FCA Mapping

Elements and Properties

ConAn Engine

Concept Lattice

Post-Filtering

Meaningful Concepts

= = =

Magnitude

<=

Analysis

High Level Views

Modelling

Interpretation


High Level ViewsHigh Level Views

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XRay Views: Class Level

Hierarchy Schemas: Class Hierarchy Level

Collaboration Patterns: Application Level

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FCA MappingFCA Mapping

Choice of ElementsChoice of Elements

Compact Representation of DataCompact Representation of Data

Choice of PropertiesChoice of Properties

Use of Negative PropertiesUse of Negative Properties

Single or Multiple ContextsSingle or Multiple Contexts

Computation of Properties and Computation of Properties and ElementsElements

Source code model



ConAn EngineConAn Engine

Performance of Ganter algorithmPerformance of Ganter algorithm

Performance of Lattice Building Performance of Lattice Building AlgorithmAlgorithm

Unnecessary propertiesUnnecessary properties

Post-FilteringPost-Filtering

Removal of Top and Bottom ConceptsRemoval of Top and Bottom Concepts

Removal of Meaningless ConceptsRemoval of Meaningless Concepts


Concept Lattice

Concept Lattice

Meaningful Concepts


AnalysisAnalysis

Concept Interpretation based on Concept Interpretation based on Elements and PropertiesElements and Properties

Equivalent ConceptsEquivalent Concepts

Automated Concept InterpretationAutomated Concept Interpretation

Use of Partial Order in the LatticeUse of Partial Order in the Lattice

Limit of using FCA as a grouping Limit of using FCA as a grouping techniquetechnique

Meaningful concepts

High Level Views


XRay Views on ClassesXRay Views on Classes

“XRay Views” is a set of collaborations to show patterns based on state and behavior

usesuses aa bb cc dd ee

m1()m1() xx xx xx

m2()m2() xx xx xx

m3()m3() xx xx

m4()m4() xx

m5()m5() xx

m6()m6() xx xx

Properties: Access to instance variables

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Class A

a, b, c, d, e

m1() { .. a .. b .. d }m2() { .. a .. b .. e }

Bottom

Top

(m1,m2),

(a,b)

(m1,m3,m6), (d)

(m1), (a,b,d)


Issues on X-Ray ViewsIssues on X-Ray Views Compact Representation of DataCompact Representation of Data

m {…m {… self n; self n; self n }self n }

Multiple FCA Contexts: Use of State and BehaviorMultiple FCA Contexts: Use of State and Behavior

Unnecessary Properties: Unnecessary Properties: isAbstract, isAbstract, isStateless, isInterfaceisStateless, isInterface

Meaningless ConceptsMeaningless ConceptsMethods: {m,n,p,q}Methods: {m,n,p,q}Concept: {m} {n,p,q}Concept: {m} {n,p,q}


Schemas on Class HierarchiesSchemas on Class Hierarchies

selfself supersuper ancestorancestor locallocal descendantdescendant

m1m1 xx xx

m2m2 xx xx xx

m3m3 xx xx xx

m4m4 xx xx xx

Type of calls – Definer Class

invo

cati

on

s

B Cm1 { ... }m2 { ... }... { self m1 }... { super m3 }

m1 { ... }m4 { ... }... { self m1 }... { self m4 }... { super m3 }

Am3 { .... }

m2 {<abstract>}

m4 {<abstract>}

Analysis of Class Hierarchies based on state and behavior of a class to identify common, bad smell and irregularity design decisions


Issues on SchemasIssues on Schemas

Single ContextSingle ContextCompact Representation of Data:Compact Representation of Data:

Class CClass Cm { ..... self p ....} m { ..... self p ....} n { ... self p ... }n { ... self p ... }

Use of negative Properties: Use of negative Properties: no-local, no-local, no-ancestor, no-descendantno-ancestor, no-descendantMeaningless Concepts:Meaningless Concepts:

Concept: { m, n, p, q } { local }Concept: { m, n, p, q } { local }Concept: { b, c , d } { no-ancestor }Concept: { b, c , d } { no-ancestor }


(1,2)(1,2)SubSub (3,2)(3,2)SubSub (1,2)(1,2)AccAcc (3,2)(3,2)AccAcc (2)(2)AbsAbs

(C,A,P)(C,A,P) xx xx xx xx

(C,A,B)(C,A,B) xx xx xx xx

(Z,X,P)(Z,X,P) xx xx xx

(Z,X,Y)(Z,X,Y) xx xx xx xx

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ple

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f cl

asse

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Properties: Relationships between classes

1 3

(1,2)Subclass

(3,2)Subclass

(2)Abstract

(3,2)Accesses

2

C P

A

Z Y

X

,=

A

C P

2

1 3

Collaboration Patterns on Collaboration Patterns on Object-Oriented ApplicationsObject-Oriented Applications


Issues on Collaboration PatternsIssues on Collaboration Patterns

Choice of Elements and Properties:Choice of Elements and Properties:(C A P) (1,2)(C A P) (1,2)subclasssubclass (1,3) (1,3)accessaccess

Compact Representation of Data:Compact Representation of Data:(C A P) (P C A) (P C A)(C A P) (P C A) (P C A)

Meaningless Concepts:Meaningless Concepts:(C A B) (Z X P) (1,2)(C A B) (Z X P) (1,2)subclasssubclass (2,1) (2,1)accessaccess

Mapping Partial Order of the Lattice: Mapping Partial Order of the Lattice:

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A

B C

A

B C

Almost Pattern

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Overloaded Pattern

Subconcept Concept Superconcept


FCA is a useful technique FCA is a useful technique in Reverse Engineering …in Reverse Engineering …

Lack of a general methodologyLack of a general methodology

Modelling software entities as FCA Modelling software entities as FCA componentscomponents

Performance of FCA algorithmsPerformance of FCA algorithms

Supporting Software EngineersSupporting Software Engineers

Interpretation of the ConceptsInterpretation of the Concepts

Use of the Complete LatticeUse of the Complete Lattice

Use of the Partial OrderUse of the Partial Order


A successful application of FCA ….A successful application of FCA ….

High-level ViewsHigh-level Views: XRay Views, Hierarchy : XRay Views, Hierarchy

Schemas, Collaboration PatternsSchemas, Collaboration Patterns

Development of a methodology Development of a methodology

Tool support of the Methodology: ConAnTool support of the Methodology: ConAn

Analysis of advantages and constraintsAnalysis of advantages and constraints

Interpretation without any mathematical Interpretation without any mathematical

knowlegeknowlege


Thank you !!Thank you !!

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Questions ?Questions ?

Arev05c icfca05lessonslearned

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