A Review on Design Pattern Detection

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A Review on Design Pattern Detection

Rajwant Singh Rao

Department of Computer Science & Information Technology Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (C. G.)

Abstract In many object oriented software, there are recurring patterns of classes. With the help of these patterns specific design problem can be solved and object oriented design become more flexible and reusable. Design Pattern existence improve the program understanding and software maintenance. Hence a reliable design pattern mining is required. Graph Matching algorithms are useful and very general form of pattern matching to find the realistic use in several areas. In this paper we are reviewing the different graph matching algorithms to detect design patterns.

Keywords Design pattern, UML, matrix, subgraph isomorphism

I. INTRODUCTION

To reuse expert design experiences, the design patterns [1] have been extensively used by software industry. When patterns are implemented in a system, the pattern-related information is generally no longer available. To understand the systems and to modifications in them it is necessary to recover pattern instances. For graph matching there are many algorithms, we are reviewing some of the algorithms which can be applied for design pattern detection. The details of each of the algorithms are discussed in sections.

  1. Exact graph matching algorithms

    Exact graph matching algorithms [7], used to find a one-to- one mapping (isomorphism) between the nodes of two graphs which have the same number of nodes so that there is also a one-to one mapping between the related edges. In the context of design pattern detection, the application of such an algorithm would require the Examination of all possible sub graphs of the system Graph that have the same number of vertices with the pattern. The most important drawback, is that a given design pattern may be implemented in various forms that differ from the basic structure found in the literature, and as a result exact matching is insufficient for design pattern detection.

  2. Inexact Graph Matching Algorithm

    The inexact graph matching procedure is used when an one to one correspondence is not found between two graphs and the purpose is to find the finest corresponding between both graphs. There are procedures which compute the edit distance between two graphs [1], defined as the number of alterations required to reach from one graph to the other graph. In the background of design pattern finding this gives incorrect results.

    In inexact graph matching process which graph takings fewer number of alterations to reach to the goal graph is supposed to the nearer to the goal graph.

  3. Kleinberg Approach for Vertices Scoring

    Kleinberg [3] offered a link analysis system for identifying pages on the web (by computing hub and authority). That is authoritative sources on broad search queries.

    The hubs can be defined as pages which points to several good authorities and authorities are the pages which are pointed to by several good hubs. The disadvantage of this process is that it only computes the similarity between two nodes rather than whole graph. To remove this drawback another approach is introduced, called sub graph isomorphism detection.

  4. Exact and Approximate Matches

    There is lack of research on the set of characteristics of each pattern. It needs to be checked, most of the researchers simplify the problem by making their own definitions. Most approaches give a piece of architectural design which structurally confirms to the structural characteristics of the pattern that they defined and behaviorally exhibits the expected actions that they defined. As the time over they find that all matching rules are conformed and they claim to find a match. If some of the rules are not conformed they claim a mismatch. Therefore there is a need to solve this problem. But different definition of the same pattern seems to make different search results. Some pattern candidates which are real instances are filtered out due to strict exact matches and some approaches perform approximate matches by computing the similarity degree.

    V (a). Similarity Scoring

    V (c) Comparative study between exact match and approximate matching

    Exact matching is required when the system piece is exactly the same as the pattern, i. e., a range in [0, 1] can be used as the matching degree So the exact matching selects system pieces with a matching degree of 1, and eliminates those with a degree less than 1. Exact matching approaches usually omit the effort of calculating matching degrees. While approximate matching sets a number between 0 and 1 as the threshold. For example 0.8 can be a reasonable threshold. Thus, any system piece with a matching degree equal to or higher than 0.8 is retained and others are filtered out when matching a particular pattern.

    Niere et al. [47] purposed fuzzy-belief, a value between 0 and 1, for each structural rule which express its precision. To limit the rule applications to reasonable cases, they introduce thresholds to the rules. The match with a fuzzy value lower

    than the threshold will be excluded. This helps to minimize computation load and improve the scalability. Tsantalis et al. [44] applied a similarity scoring algorithm, an inexact graph matching algorithm, to compute matching degree between system under study and the pattern, when an exact isomorphism between two graphs cannot be found. Dong et al. [6] applied a template matching algorithm to calculate the matching degree between a piece of system and a design pattern. Guéhéneuc et al. [42] propose a machine learning algorithm to compute software metrics and use the degree of confidence to infer the rules by a rule learner. Ferenc et al.

    [43] introduce predictors for each pattern and use machine learning algorithm training the pattern recognizer to acquire the value of the predictors. These predictor values are then used as the standards to compare with the predictor values

    13 obtained for system under study. A pre-processing algorithm is also proposed by Dong et al. [48] for generating the training set of machine learning algorithms for pattern mining. Table 1 gives the categorization of different tools on exact or approximate matching.

    Matching Technique

    Authors

    Tools

    Exact Match

    Kramer 1996 [12]

    Pat

    Seemann 1998[19]

    Bansiya 1998[8]

    DP++

    Antoniol 1998 [14]

    Tonella 1999 [20]

    Keller 1999 [10]

    SPOOL

    Blewitt 2001 [21]

    Hedgehog

    Mei 2001 [22]

    JBOORET

    Albin-Amiot 2001 [23]

    Ptidej

    Asencio 2002 [24]

    Wendehals 2003 [25]

    Smith 2003 [26]

    SPQR

    Heuzeroth 2003 [27][28]

    Beyer 2003 [29]

    CroCoPat

    Park 2004 [30]

    Zhang 2004 [31]

    Costagliola 2005 [32][33]

    DPRE

    Streitferdt 2005 [34]

    Huang 2005 [35]

    PRAssistor

    Wang 2005 [36]

    DPVK

    Kaczor 2006 [37]

    Ptidej

    Shi 2006 [38]

    PINOT

    Dong 2007 [39][40]

    DP-Miner

    Approximate Match

    Niere 2002 [41]

    FUJABA

    Guéhéneuc 2004 [42]

    Ferenc 2005 [43]

    Columbus

    Tsantalis 2006 [44]

    Table 1 Categorization of Current Discovery Method Based on Matching Techniques [48]

    1. Sub Graph Isomorphism Detection

      1. Theoretical Approach

        The subgraph isomorphism is a convenient generalization of graph isomorphism. The subgraph isomorphism problem [4] is define whether a graph is isomorphic to a subgraph of any other graph. Consider [5] G1 (V1, E1) and G2 (V2, E2) be two graphs, where V1, V2 are the set of nodes and E1, E2 are the set of edges. Let M1 and M2 be the adjacency matrices of dimensions m x m and n x n, where m < =n, corresponding to the graphs G1 and G2 respectively. A

        permutation matrix is a square (0, 1)-matrix that has exactly one entry 1 in each row and each column and 0's elsewhere. Two graphs G1 (M1, Lv, Le) and G2 (M2, Lv, Le) are said to be isomorphic [5] if there exist a permutation matrix P such that

        M2 = P M1 PT (1)

        A subgraph S of a graph G, G, is a graph S = (Mi, Lv, Le) where Mi = Sm,m(P M PT) is an m x m adjacency matrix for some permutation matrix P.

        There is a subgraph isomorphism from G1 to G2 if there exists an n x n permutation matrix P such that

        M1 = Sm,m(P M2 PT) (2)

        We take M2 matrix as a system design matrix and we guess nondeterministically M1 as a design pattern matrix. And then try to find out whether M1 is subisomorphic to M2 or not or it can be easily said whether there exist design pattern in the system graph or not.

        Hence the problem of finding a subgraph isomorphism from graph G1 to G2 is equivalent to finding a permutation matrix for which equation (2) holds. Thus, we generate permutation adjacency matrix of a model graph (system under study) one by one and check whether equation (2) holds or not, when it holds we stop and declare that that particular design pattern has been detected. It can be also possible that there is no design pattern exists in system graph. In this case we find no permutation matrix for which equation (2) holds.

    2. Searching for minimal key structures

      In this method a defined key structure is associated to each pattern. The key structure for pattern describes the number of minimum classes and objects that are present in that structure. By define the key structure of pattern the pattern can be securely identified. The properties of key structure are used as the search criteria. There are three software systems for automated searching is known which are based on this approach. These are DP++[8], for C++, KT [9] for Smalltalk and SPOOL [10] realized for C++, applicable for Java and Smalltalk.

      The DP++[8] searches the following design patterns: COMPOSITE, DECORATOR, ADAPTER, FACADE, BRIDGE, FLYWEIGHT, TEMPLATE METHOD and

      CHAIN OF RESPONSIBILITY. DP++ [8] is not applicable for other patterns. This tool has three parts: (i) C++ Code Translation Subsystem used for analyzing source code. (ii) Pattern Detection Subsystem used for the recognition of generation patterns and (iii) Display Subsystem used for the visualization of detected patterns. Information about the achieved values of recall and precision is not available.

      KT [9] is able to search COMPOSITE, DECORATOR, STATE, STRATEGY, COMMAND, TEMPLATE METHOD and CHAIN OF RESPONSIBILITY patterns. It

      is unable to recognize INTERPRETER pattern .The information about other patterns are not mentioned.

      Information about the achieved values of recall and precision is not available.

      SPOOL [10] is capable of searching BRIDGE, FACTORYMETHOD and TEMPLATEMETHOD patterns.

    3. Searching for class structures

This approach uses the pattern class structures described by Gamma patterns [11].For example consider the following figure 18. This is the composite pattern. A composition pattern exit if a class has at least two subclasses and one of them has 1 to n aggregation to the super class. There are three software systems for automated search which are based on this approach. These are Pat [12] for C++, IDEA [13] for UML diagrams and the multi-step search tool [14].

A

B

C

Fig.18. A Composite Pattern

patterns, such as Kramer [12] and Costagliola [32][33], usually discover structural patterns with few behavioral patterns. Approaches which takes both structural and behavioral patterns, such as Heuzeroth et al. [27][28], are able to discover more behavioral patterns.

XII. Experiments

To evaluate the different approaches experiment is the good way. Table 4 shows a list of software system used in the experiments by different studies. It is difficult to evaluate the precision value of different approaches since there is a lack

Pat [12] describes the pattern class structure by PROLOG rules. This tool is able to finds ADAPTER, PROXY, BRIDGE, DECORATOR and COMPOSITE patterns. This search tool has been been tested with software systems containing 9343 classes. The achieved recall value in each case is 100% and the average precision value is 36.75%. IDEA [13] based on UML search approach which uses class and collaboration diagrams. It also used PROLOG rules. This is able to find the following patterns: TEMPLATE METOD, PROXY, ADAPTER, BRIDGE, COMPOSITE, DECORATOR, FACTORY METHOD, ABSTRACT FACTORY, ITERATOR, OBSERVER and PROTOTYPE.

The multi step search tool [14] is able to find the following patterns ADAPTER, BRIDGE, PROXY, COMPOSITE and

DECORATOR. This is unable to recognize the other patterns.

The multi step search tool was tested with different C++ Libraries [14]. The achieved recall value is 100% and an average precision value is 35%..

  1. Discovered Patterns

    Since there are large numbers of available patterns, each approach often considers only a small number of patterns. The following table 3 shows the summarization of patterns discovered by different tools. This table show that the approaches that focus to the structural aspect of

    of document of these systems, especially for open source system.

  2. Analysis of Experiment Results

As shown in Table 2 and Table 4 most approaches given experiments on mining different patterns from some application systems. Based on study in the previous section we found that different approaches give different results when mining the same pattern in the same system. For instance, Table 5 shows the comparison of the mining results of the same design patterns from the same systems by two different approaches.

Authors

Tools

Languag e

Structural (ST) Behavioral (BE) Semantic (SE)

Exact (EX) Approximate (AP)

match

Autom- atic(AT) Inter- active(IT)

Techniques

System Representation

Pattern Representatio n

Kramer 1996 [12]

Pat

C++

ST

EX

AT

Prolog

Prolog

Prolog

Seemann 1998[19]

Java

ST

EX

AT

first order logic, graph

Graph

Predicate

Bansiya 1998[8]

DP++

C++

ST

EX

AT

class hierarchy

text

Antoniol 1998 [14]

C++

ST & BE

EX

AT/p>

metrics

AST

AOL

Tonella 1999 [20]

C++

ST & BE

EX

AT

concept analysis

Keller 1999 [10]

SPOOL

C++

EX

IT

UML/CDIF

Blewitt 2001 [21]

Hedgeh og

Java

ST & BE & SE

EX

AT

Spine

Mei 2001 [22]

JBOOR ET

C++

ST

EX

Albin-Amiot 2001

[23]

Ptidej

Java

EX

AT

CSP

Asencio 2002 [24]

C++

ST

EX

IT

Wendehals 2003

[25]

Java

ST & BE

EX

AT

dynamic runtime data

ASG & call graph

Smith 2003 [26]

SPQR

C++

ST

EX

AT

OTTER rho- calculus

OML & OTTER

otter rules

Heuzeroth 2003

[27][28]

Java

ST & BE

EX

AT

SanD and SanD- Prolog

AST

AST &TLA

Beyer 2003 [29]

CroCoP at

Java

ST

EX

AT

predicate calculus

BDDs

predicates

Park 2004 [30]

BE

EX

AT

Class Diagram

Zhang 2004 [31]

ST

EX

AT

Graph (matrix)

Graph (matrix)

Costagliola 2005

[32][33]

DPRE

C++

Java

ST

EX

AT

XPG formalism & LRbased

parsing

SVG & AOL-

>AST

Grammar- based Pattern Specification

Streitferdt 2005

[34]

Java

ST

EX

AT

Huang 2005 35]

PRAssi stor

EX

AT

Wang 2005 [36]

DPVK

ST & BE

EX

AT

REQL query

REQL static & RSF

dynamic

REQL script

Kaczor 2006 [37]

Ptidej

Java

ST

EX

AT

bit representation

bit representation

Shi 2006 [38]

PINOT

Java

ST & BE

EX

AT

Data/Control Flows

AST

DFG & CFG

Dong 2007

[39][40]

DP-

Miner

Java

ST & BE & SE

EX

AT

Matrix and Weight

XMI

XMI

Niere 2002 [41]

FUJAB A

Java

AP

IT

bottom-up & top- down

ASG

ASG

Guéhéneuc 2004

[42]

Java

ST

AP

AT

XML tree & PADL

PADL

Ferenc 2005 [43]

Columb us

C++

ST

AP

AT

ASG, XML DOM

tree

DPML

Tsantalis 2006 [44]

Java

ST

Ap

AT

Similarity Matrix

matrix

Table 2 Comparison of Pattern Recovery Approaches

Authors

Abstract Factory

Adapter/Command

Builder

Bridge

Chain of Responsibilities

Command

Composite

Decorator

Facade

Factory Method

Flyweight

Mediator

Observer/MVC

Prototype

Proxy

Singleton

Strategy/State

Template Method

Visitor

Kramer 1996 [12]

×

×

×

×

×

Seemann 1998[19]

×

×

×

Antoniol 1998 [14]

×

×

×

×

×

Keller 1999 [10]

×

×

×

Blewitt 2001 [21]

×

×

×

×

×

Asencio 2002 [24]

×

×

×

×

×

×

×

Heuzeroth 2003

[27][28]

×

×

×

×

×

×

Balanyi 2003 [45]

×

×

×

×

×

×

×

×

×

×

×

×

Hericko 2005 [46]

×

×

×

Costagliola 2005 [32] [33]

×

×

×

×

×

Huang 2005 [35]

×

×

×

×

×

×

×

×

×

×

×

×

×

×

×

Kaczor 2006 [37]

×

×

Shi 2006 [38]

×

×

×

×

×

×

×

×

×

×

×

×

×

×

×

×

Dong 2007 [39][40]

×

×

×

×

Niere 2002 [41]

×

×

×

Guéhéneuc 2004 [42]

×

×

×

×

×

×

×

×

×

×

×

×

×

Ferenc 2005 [43]

×

×

Tsantalis 2006 [44]

×

×

×

×

×

×

×

×

×

×

×

Table 3 Design Patterns Discovered by Different Approaches

Authors

Galib

LEDA

Libg++

Java AWT

Java Swing

JDK

JEdit

JHotDraw

JRefactory

JUnit

Mec

QuickUML

Socket

zApp class library

Other

Kramer 1996 [12]

×

×

Seemann 1998[19]

×

Antoniol 1998 [14]

×

×

×

×

×

×

Tonella 1999 [20]

×

Keller 1999 [10]

×

Blewitt 2001 [21]

×

Albin-Amiot 2001 [23]

×

×

×

×

Asencio 2002 [24]

×

Beyer 2003 [29]

×

×

×

×

Heuzeroth 2003 [27][28]

×

×

Balanyi 2003 [45]

×

×

Costagliola 2005 [32] [33]

×

×

×

×

Streitferdt 2005 [34]

×

×

Huang 2005 [35]

×

×

Kaczor 2006 [37]

×

×

×

Shi 2006 [38]

×

×

×

×

Dong 2007 [39][40]

×

×

×

×

Niere 2002 [41]

×

×

Guéhéneuc 2004 [42]

×

×

×

×

×

Ferenc 2005 [43]

×

Wang 2005 [36]

×

Tsantalis 2006 [44]

×

×

×

Table 4 Experiments Done in Different Studies

Systems

JHotDraw5.1

JRefactory2.6.24

JUnit3.7

Authors

Tsantalis

et al. 2006 [44]

Guéhéneuc

et al. 2004 [42]

Tsantalis

et al. 2006 [44]

Guéhéneuc

et al. 2004 [42]

Tsantalis

et al. 2006 [44]

Guéhéneuc

et al. 2004 [42]

Adapter

18

1

7

7

1

0

Composite

1

1

0

0

1

1

Decorator

3

1

1

0

1

1

Factory Method

3

3

4

1

0

0

Observer

5

2

0

0

4

3

Prototype

1

2

0

0

0

0

Singleton

2

2

12

2

0

2

State

23

2

12

2

3

0

Template

Method

5

2

17

0

1

0

Visitor

1

0

2

2

0

0

Table 5 Different Results from the Same System of the Same Version

CONCLUSION:

The exact graph matching, inexact graph matching and Kleinberg Approach for vertices scoring shows the similarity between nodes not in the whole graph. This drawback, is reduced by sub graph isomorphism detection. The subgraph isomorphism shows whether a graph is isomorphic to a subgraph of any other graph or not .It uses

the concept of Overall matrix to reduce the number of manipulations, it combines different matrices (like generalization matrix, association matrix, dependency matrix, aggregation matrix etc) into a single matrix. So there will be only two overall matrices, one corresponding to system and one for design pattern. After it we try to find out

whether a particular design pattern exists on the given graph. This paper also shows different table on several studies.

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