Placement of Entities in Object-oriented Systems by means of a Single-objective Genetic

Algorithm

Margaritis Basdavanos Alexander Chatzigeorgiou

University of Macedonia

Thessaloniki, Greece

5th International Conference on Software Engineering Advances (ICSEA’2010)

Design

System

• The decomposition into modules

• The design (in any technical field) consists in:

Design

• The design (in any technical field) consists in:

• The decomposition into modules

System

• The definition of the relationships among them

Design

• The design (in any technical field) consists in:

• The decomposition into modules

System

• The definition of the relationships among them

Functionality AFields: X, Y

Functionality BFields: Y, Z

• The assignment of responsibilities and state information

“Old” problem

• The distribution of responsibilities and state information among system modules is an “old” problem in SE

How to achieve an optimal design?

“Old” problem – Why is it important?

Coupling Cohesion

Why is coupling and cohesion important?

• Empirical studies have shown that they affect a large number of –ilities in OO design

• comprehensibility

• maintainability

• testability

• reusability

Attributes in Quality Models €

Software Ageing

well-designed code

Design quality decays

Problem

• Given a system S : (methods, attributes, classes)

• Find an optimal assignment that

• minimizes coupling and

• maximizes cohesion

• Search problem in the space of alternative

arrangements

Optimal Placement

• Placing 4 entities into 2 classes

+m1()

-x

A

+m2()

-y

B1η

+m2()

-y

A

+m1()

-x

B2η

+m1()

A

+m2()

-y-x

B3η

+m1()

-x-y

A

+m2()

B4η

+m1()+m2()

-x-y

A B

5η

+m2()

-x

A

+m1()

-y

B6η

For x classes and n entities the search space consists of xn possible allocations !!

Crash Course on Genetic Algorithms

GAs are heuristics that can generate useful solutions to optimization and search problems

Inspired by evolution and natural selection (Darwin's theory)

Crash Course on GAs – Brief Overview

Select initial population of individuals

Evaluate the fitness

of each individual

in that population

Breed new individuals through

crossover and mutation operations

Crash Course on Gas - Terminology

Potential Solutions are usually encoded as binary or integer strings called chromosomes

chromosome

Elements of chromosomes are called genes

genes

The values that a gene can take are called alleles

1 2 1 2 3

alleles

The positions of genes in the chromosome are called loci

0 1 2 3 4 5 6 7 8 9loci

Our fitness function: Entity Placement

• Constituents of an OO system: methods and attributes

• Methods can access attributes and methods of the class that they belong to directly and also attributes and methods of other classes through references.

•Attributes can be accessed directly from methods of the class that they belong to and also from methods of other classes that have reference to that class.

• For each entity it is possible to define a set of entities that it accesses (if it is a method) or a set of entities that it is accessed from (if it is an attribute).

• Entity Set of a Class: contains all of its members

Entity Placement

• It is reasonable to assume that the similarity between an entity and a class is high when the number of common entities is large

• Jaccard Distance

BA

BABAcetandis

1,

Example of Distance Calculation

16

f1

Sm = {Source.field, Target.f1, Target.m2} STarget = {Target.f1, Target.m2, Target.m1}

field

Sm STarget = {Target.f1, Target.m2}

Sm STarget = {Source.field, Target.f1, Target.m2, Target.m1}

distance(Sm , STarget) = 1 – 2/4 = 0.5

Global Measure of Entity Placement

Centities

Cecetandis

Centities

Cecetandis

ementEntityPlac

Cej

Cei

C

j

i

,

,

i

i

CC

iSystem ementEntityPlac

entitiesall

CentitiesementEntityPlac

cohesion

coupling

Simplified Case Studies

• Entity Sets Class SetsSx = {m2} => S0 = {3} SA = { x, m1} => S0 = {0,1}Sm1 = {y} => S1 = {2} SB = { y, m2} => S1 = {2,3}

Sy = {m1} => S2 = {1} Sm2 = {x} => S3 = {0}

+m1()

-x

A

+m2()

-y

BClass 0 Class 1

0

1

2

3

Simplified Case Studies

• Chromosome of the initial model

• System Entity Placement = 2

• Number of possible arrangements: 24 = 16

A A BB

x m1 y m2

0 0 1 1

0 1 2 3

Αποκωδικοποιημένη μορφή Κωδικοποιημένη μορφήDecoded Form Encoded Form

Optimal Placement

• Placing 4 entities into 2 classes

+m1()

-x

A

+m2()

-y

B1η

+m2()

-y

A

+m1()

-x

B2η

+m1()

-x-y

A

+m2()

B4η

+m1()+m2()

-x-y

A B

5η

+m2()

-x

A

+m1()

-y

B6η

+m1()

A

+m2()

-y-x

B3η

A A BB

x m1 y m2

B B AA

x m1 y m2

B A BB

x m1 y m2

A A BA

x m1 y m2

A A AA

x m1 y m2

A B AB

x m1 y m2

Simplified Case Studies

• GA population size 2*m = 2*4 = 8• Crossover Rate:50%, Mutation Rate: 12,5% Selection rate: 80%

– Chromosome of the optimal solution

• Resulting Entity Placement value = 0– Average execution time: 14 ms (Pentium IV 3,2GHz)

– Average number of generations : 3

A B AB

x m1 y m2

0 1 1 0

0 1 2 3

Αποκωδικοποιημένη μορφή Κωδικοποιημένη μορφή

Simplified Case Studies

0

1

2

3 +m2()

-x

A

+m1()

-y

BClass 0 Class 1

Optimal Class Diagram

Evolution of the fitness function

Simplified Case Studies - #2

+addOrder()+getFirstOrder()+removeFirstOrder()+getOrderCodes()+calculateTime()+findPosition()+calculatePlatesWaiting()+getOrders()

Queue

+Order()+getCode()+pickPlate()+addPlate()+getPlates()+getNumberOfPlates()

-code

Order

-ordersWaiting1

*

+setPlateName()+getPlateName()+storeIngrendient()+isAvailable()+pick()

-plateName

Plate-plates

1 *

+Ingredient()+checkQuantity()+removeIngredient()+getIngredientName()

-ingredientName-amount

Ingredient

-map1*

+addToPlateCatalog()+getPlate()+getPlateNames()

PlateCatalog

-catalogPlates1

*

+addToIngredientCatalog()+getIngredient()+getIngredientNames()

IngredientCatalog

-ingredients

1*

2

0

3

4

5

6

1

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34 35

36

37

0

1

2

4

5

3

Simplified Case Studies - #2

• Chromosome for OrderManagement

Κωδικοποιημένη κατανομή οντοτήτων στις κλάσεις του συστήματος

• Entity Placement OrderManagement = 0,7141

• Number of possible arrangements: 638

10 110 0 0 00 10 11 2 22 22 2 2 3 3 3 3 3 3 3 3 3 4 4 4 4 5 5 5 52

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Simplified Case Studies - #2

• GA population [1,2*38] = 46

– Configuration: pc = 50% pm = 2,63% ps = 60%

• Final Entity Placement value = 0,6878

– Average execution time: 15,7 s (Pentium IV 3,2GHz)

– Number of generations : 86

Simplified Case Studies - #2

Evolution of Entity Placement

Eclipse Plugin

Project to be

analyzed

Analysis by clicking here

Results of the GA

Eclipse Plugin

Conclusions

• Optimal solutions can be reached within reasonable time

(much faster than Multi-Objective GAs)

• Feasible distribution of entities among classes avoiding the

generation of “God” classes

• Entity Placement captures efficiently coupling and

cohesion

Future Work

• Handling of user constraints

• Proper handling of inheritance

• Empirical Studies on large systems

Thank you for your attention !!

5th International Conference on Software Engineering Advances (ICSEA’2010)