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A Review on Design Pattern Detection

Profile image of Rajwant RaoRajwant Rao

2019

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

Figures (7)
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.
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.
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 | 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]. 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.
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 | 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]. 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.
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. 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
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. 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
Table 2 Comparison of Pattern Recovery Approaches
Table 2 Comparison of Pattern Recovery Approaches
Table 3 Design Patterns Discovered by Different Approaches
Table 3 Design Patterns Discovered by Different Approaches
Table 4 Experiments Done in Different Studies 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
Table 4 Experiments Done in Different Studies 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

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