Identification of design motifs with pattern matching algorithmsse.kaist.ac.kr › wp-content ›...

Identification of design motifs with pattern matching

algorithms

Information and Software Technology (2010)Oliver Kaczor, Yann-Gaël Guéhéneuc, and Sylvie Hamel

2010. 01. 20.Presented by Seul-Ki Lee

Contents

IntroductionRelated workPre-processing stepIdentification step

Automata simulationBit-vector processing algorithm

Case studyConclusionDiscussion

ⓒKAIST SE LAB 2010 2/22

Introduction (1/3)

Software maintenance and evolution time- and resource-consuming activity

• More than 50% of the total cost of the systems• Documentation is often useless and lost

Main tasks of maintainer• Design recovery

– Build higher-level abstraction from code to understand system– Benefit from the design pattern used by developer

0%20%40%60%80%

100%

Zelkowits et al,

[1979]

Lients & Swarson,

[1981]

McKee, [1984]

Port, [1988]

Huff, [1990]

Moad, [1990]

Eastwood, [1993]

Erlikh, [2000]

(studied year)

(cost ratio)

cost ratio = maintenance & evolution cost / total cost

3/22

Introduction (2/3)

Design patterns and design motifsSolution to recurring design problems

• 23 patterns by GoF(Gang of Four) and other patterns

Micro-architecturesConcrete indication of some design motifs in the implemented systemSynonym with the set of motifs

Design patterns

Design motifs

Not observable(intent, motivation, applicability, consequences)

Observable

(structure, participants, collaborations)


Introduction (3/3)

ObjectivesIdentify the micro-architectures

• Adapt two classical approximate string matching algorithms– Automata simulation– Bit-vector processing

• Set the four approximate types to identify efficiently


Related work (1/2)

Yann-Gaël Guéhéneuc et al. [TSE, 2008]Combination of machine learning algorithms and explanation-based programming

• Remove search space by quantifying the micro-architectures• Apply explanation-based programming to identify micro-

architectures on remaining entities

Limitation• Performance of explanation-based constraint programming


Related work (2/2)

Tsantalis et al. [TSE, 2006]Combination of graphs and matrices

• Convert the graphs representing the structure of a motif• Use matrices representing entities and relationship• Compute similarity scoring between matrices

Limitation• Hard to descript the possible approximations• Exist some errors in the reported results

– There are some wrong results


Automata simulation

Overall processing step

Systems Design motifs Inputs

Pre-processing

Identification processing with approximation

OutputsIdentifiedoccurrences

Represented byUML-like models

1. Adjust constraints to reduce false positive2. Transform models to Eulerian graphs3. Transform graphs to strings

Bit-vector processing

Compared with true occurrences (TO)


Pre-processing (1/3)

Step1. Adjust ignorance relationship to reduce false positive occurrencesand describe to PADL meta-model

Relationship type

as : associationag : aggregationco : compositiondm : dummyin : inheritanceig : ignorance



UML-like model of systemUML-like model of design motif

Eulerian model of systemEulerian model of design motif

Step2. Transform to Eulerian graphs by adding dummy relationships


Eulerian model of systemEulerian model of design motif

Step3. Transform to strings by using directed Chinese Postman problem, shortest tour of a graph

Component in Leaf dm Component in Composite co Leaf A in B in D dm B in E co B in C dm G cr C dm G cr D dm G cr E dm G as F ag A

Strings of Eulerian models


Identification process (1/3)

Automata simulation

Definition of occurrences of motif• Each path to the final state

Limitation• Hard to manage a very large number of paths• Hard to handle ignorance relationships

A in B in D dm B in E co B in C dm G cr C dm G cr D dm G cr E dm G as F ag A

B D D B

B E

E B



Bit-vector processing algorithmCharacteristics

• : token, : string

G=

• Apply standard bit operations– Bit-wise logical “and”, “or” operators– Left ( ) and right ( ) circular shifts

• Use negation operator– Easy to handle ignorance relationships


0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0



Bit-vector processing algorithm (cont’d)Compare micro-architecture with design motif


Component in Leaf dm Component in Composite co Leaf

TripletsFirst (in) Third (in) Fourth (co)

Component Leaf LeafComp

onentComposite LeafComp

onentComposite

A B A B B B C EB C B C C B D EB D B C D B E EB E B C E

B D CB D DB D EB E CB E DB E E

14/22

Case study (1/5)

EnvironmentSelected design motifs

• Abstract Factory, Composite, Decorator, Observer, State/Strategy

Systems• Seven open-source applications• Wide range of domains• Small to medium (57 classes to 742 classes)

Adjusted approximation• Replace a stronger or weaker relationships• Allow entities adding in or removing from the hierarchy• Change the role between abstract and concrete entities• Accept cases that all roles are not played in the design motif


Case study (2/5)

Computation timeAbstract Factory design motif

Composite design motif


Case study (3/5)

Definition of metrics to measure Precision

Recall


Case study (4/5)

Comparison between identified occurrencesAbstract Factory design motifJHotDraw QuickUML

Composite design motifJHotDraw QuickUML

TO CP+M BV0 5235 250

Precision 0.00% 0.00%

Recall 0.00% 0.00%

Time 37,922 s 32 s

TO CP+M BV8 1159 11

Precision 100.00% 100.00%

Recall 100.0% 100.00%

Time 20,320 s 97 s

TO CP+M BV52 115 103

Precision 100.00% 100.00%

Recall 100.00% 100.00%

Time 907 s 3 s

TO CP+M BV8 1159 11

Precision 100.00% 100.00%

Recall 100.00% 100.00%

Time 1164 s 28 s

TO (true occurrences)CP+M (constraint programming with metrics)BV (bit-vector processing algorithm)ⓒKAIST SE LAB 2010 18/22

Case study (5/5)

Comparison between identified occurrences (same definition of an occurrence as Tsantalis et al)

Abstract Factory design motifJHotDraw QuickUML

Composite design motifJHotDraw QuickUML

TO SC BV0 - 250

Precision - 0.00%

Recall - 100.00%

TO SC BV8 - 11

Precision - 72.73%

Recall - 100.00%

TO SC BV2 1 8

Precision 100.00% 25.00%

Recall 50.00% 100.00%

TO SC BV2 1 2

Precision 100.00% 100.00%

Recall 50.00% 100.00%

TO (true occurrences)SC (scoring algorithm)BV (bit-vector processing algorithm)ⓒKAIST SE LAB 2010 19/22

Conclusion

SummaryPresent two pattern matching algorithms to identify design motifs

• Automata simulation – not different result on performance• Bit-vector processing – better performance than previous works

Allow to adjust approximation adequately

Future workUse of metric-based analysis to reduce search spaceData integration related to method and field declarationsMore experiments on even larger systems and other patterns (anti-patterns)


Discussion

ContributionSuggest new approach to identify design patterns

• Provide interesting performance, precision and recall with respect to previous approaches

• Present that automata simulation seems too slow to be useful• Suggest four approximations to identify design motifs flexibly

LimitationLimited precision when considering the developers’ intentsDifficulty on identifying the Singleton motifLack of approximations based on method type or field access


Thank you

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