DETERMINE COHESION AND COUPLING FOR CLASS DIAGRAM THROUGH SLICING TECHNIQUES

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1 IJACE: Volume 4, No. 1, January-June 2012, pp DETERMINE COHESION AND COUPLING FOR CLASS DIAGRAM THROUGH SLICING TECHNIQUES Akhilesh Kumar 1* & Sunint Kaur Khalsa 1 Abstract: High cohesion or module strength indicates that a system has been well partitioned into components which have strong internal relationships between attribute, method and class. Cohesion is an important factor in term of software design. Coupling indicates the degree of interdependence among the component of a software system. Coupling is thought to be a desirable goal in software construction, leading to better value of internal attribute, such as maintainability, reusability and reliability. A coupling metrics capture the degree of interaction and relationship among class dependency graph element attribute and method in software system. The Slicing technique using to slice the class dependency graph, Program slicing describes a mechanism which allows the automatic generation of a slice. In this paper, we proposed a new technique for Cohesion and Coupling for Class Diagram through Slicing Techniques. Proposed new techniques show the class dependency graph, dependency between attribute-attribute, attribute-method and method-method. Result indicate that the propose scheme significantly low coupling and high cohesion, so system are more reliable, efficient and credibility of class diagram is appropriate. Keywords: Cohesion, Coupling, Class Slicing, Class Dependency Graph, Static Slicing, Dynamic Slicing, CBCD. 1. INTRODUCTION Cohesion and Coupling are an important factor in term of software design. Coupling indicates the degree of interdependence among the component of a software system. Classes play an important role in the objectoriented based system design. Entities in an application domain are captured as classes and applications are built with the objects which are instantiated of class.unified Modelling Language (UML) play the important role building of Object-Oriented software. UML divided into two general sets: structural modelling diagrams and behavioural modelling diagrams. Part one will deal with structural modelling diagrams. Class Diagrams show the different classes that make up a system and how they relate to each other. Class Diagrams are said to be static diagrams because they show the classes, along with their methods and attributes as well as the static relationships between them. We have done our Class dependency graph (CDG) representation of UML class diagrams [11]. A CDG represents a set of classes and their relationships. Coupling represent to the degree of interdependence among the components of a class diagram of software system [5]. Good software design should have low 1 Guru Nanak Dev Engineering College Ludhiana Punjab (India), ( * akhilesh1987@gmail.com) coupling but higher coupling makes a system more complex, highly inter-related modules are difficult to understand. Cohesion indicates module strength in a system. High cohesion or module strength indicates that a system has been well partitioned into components which have strong internal relationships between attribute, method and class. Cohesion is an important factor in term of software design. The Class cohesion is the property of connectivity among the aggregate elements of a single class. Mostly class cohesion measures are based on an abstract representation that represents the method-attribute reference relationships and/or methods similarity relationships and they calculate the class cohesion based on the number of edges in the graph [2, 6]. Class slicing is a decomposition technique that removes class components not relevant to a chosen computation, referred to as a slicing criterion. The remaining components form an executable class called a slice that computes a projection of the original class s semantics. The class slice consists of the parts of a class that may affect the values computed at some point of interest, referred to as a slicing criterion. Class slicing can be used in debugging to locate source of errors more easily. Class slicing describes a mechanism which allows the automatic generation of a slice. All Statements

2 20 Akhilesh Kumar & Sunint Kaur Khalsa affecting or affected by the variables mentioned in the slicing criterion becomes a part of the slice. The slices based coupling and cohesion determine on the basis of dependencies among the class slicing and design matrix for an UML model. A static slice can be computed by identifying the different architectural element and dependencies among them for an UML model. These selectively identified architectural elements can comprise classes and their objects, different attributes and the method calls. We collectively term these identified architectural elements as a slice of architecture. These architectural elements are identified based on a slicing criterion. In the following, we define a slicing criterion, and its corresponding computed static slice for an architectural model. The calculation of coupling and cohesion measures, sets of slices and their intersections comparable to the use of slice profiles in are needed. A class diagram convert into dependency graph and identified the attributes and method and its relationship. A slice applies on the basis of class slicing criterion to calculate the cohesion on the basis of cohesion metrics. In this paper, we proposed a new technique for Cohesion and Coupling for Class Diagram through Slicing Techniques. Proposed new techniques show the class dependency graph, dependency between attribute-attribute, attribute-method and method-method. New scheme show low coupling and high cohesion, so design system is more reliable, efficient and credibility of class diagram is appropriate. The rest of the paper is organised as follow, we have given the related work in section II. Design procedure for design matrics is highlighted in section III. The calculation of slice based cohesion and coupling of class dependency graph is given in section IV and V. Performance evaluation through simulation is described in section VI. Finally the conclusion is given in section VII. 2. RELATED WORK The most importance factor of software products based on design properties such as coupling, there is little work in this area. Most existing measures capture coupling between modules using source code which is only available after implementation [3]. These measures and demonstrate that the values of coupling and cohesion can also be used for assessing deterioration effects [12]. Coupling is a measure for the strength of inter-component connections, and cohesion is a measure for the mutual affinity of subcomponents of a component. Within the range of this contribution we are interested in how these measures are calculated and what they indicate. As adumbrated in the introduction, a practical way in calculating coupling and cohesion measures is to make use of slices. A coupling measure named Coupling between classes diagram (CBCD) is defined, and empirically validated in [2]. With the CBCD measure, class A is coupled to class B if A uses B s member method and/or instance variables. CBCD counts the number of classes to which a given class is coupled. Coupling refers to the degree of independence between parts of the design. To measure coupling in class diagrams there are three types of metrics. In this paper we are shows that measure coupling performance. A measure of coupling is more useful to determine the complexity of software. CBCD for a class is a count of the number of other classes to which it is coupled. The definition of CBCD deals with instance variables and total number of methods of the class. When two classes are coupled, the methods and instance variables defined in one class is used by another class. Multiple accesses to one class are counted as one access. 3. DESIGN FOR PROCEDURE FOR DESIGN METRICS A dependency is used to model a wide range of dependent relationships between model elements. It would normally be used early in the design process where it is known that there is some kind of link between two elements. The trace relationship is a specialization of a dependency. Different types of dependency but we are used the generally four types of dependency like a system dependency, class dependency, data dependency, control dependency. A class dependency is construction of class dependence graphs for single classes, derived classes and interacting classes. The section also discusses the way in which our graphs represent polymorphism. For example, a figure 1 show that Class Diagram of Employee information, employee information have a two classes first class is the employee class, in that class four attributes employee Id, first Name, last Name, , and four method set department ( ), set ( ), set first name ( ), set last name ( ), set manager ( ), set employee ( ). A department class have a four attributes department ID, Name, City, State, and four method set department Id ( ), set Name ( ), set City ( ), State ( ). Between all two classes has a different dependencies like a employee- department class many to one dependencies means many employee depend upon one department, this dependency a employee class side, one to many dependency means one department have a many employee Scenarios of Class Diagram (Employee information) In CDG, classes and their attributes, methods and their call parameters, together with method return values are represented as different types of nodes.

3 Determine Cohesion and Coupling for Class Diagram Through Slicing Techniques 21 the flow dependence between attributes is not so straight forward. Let c be a class, and its relationships. (1) If m ε M(c) and a ε RA(m), then m is read dependent on a, denoted by m a. (2) If m ε M(c) and a ε WA(m), then a is write dependent on m, denoted by a m. (3) If m1, m2 ε M(c) and m2 ε CALL(m1), then m1 is call dependent on m2, denoted by m1 m2. Figure 2: Dependency Graph of Employee Information Figure 1: Class Diagram of Employee Information These would be represented by using appropriate dependence edges in the CDG. Member dependence edges represent the class memberships of methods and attributes, while method dependence edges represent the dependence of the call parameters and return values (if any) on a method. Data dependence edges represent own of data among statements of a class method. Based the class dependency graph, the direct read dependence from methods to attributes, the direct write dependence from attributes to methods, and the direct call dependence between methods can be easily to be concluded. However, 3.2. Static Slicing of Class Dependency Graph Slices are computed using a dependence graph. Static slicing technique uses static analysis to derive slices. That is, the dependency graph is analyzed and the slices are computed for all possible input values. A state that slices based coupling and cohesion determine on the basis of dependencies among the program slicing and design matrix for an UML model. A static slice can be computed by identifying the different architectural element and the dependencies among them for an UML model. These selectively identified architectural elements can comprise classes and their objects, different attributes, and the method calls. We

4 22 Akhilesh Kumar & Sunint Kaur Khalsa collectively term these identified architectural elements as a slice of architecture. These architectural elements are identified based on a slicing criterion. In the following, we define a slicing criterion, and its corresponding computed static slice for an architectural model. For the calculation of coupling and cohesion measures, sets of slices and their intersections comparable to the use of slice profiles in are needed. Flow p ( c1, c2)( + 2, Flow 1) p 2c c Nc Coupling P ( C1, C2) = N ( c1)( N2) c Calculate coupling between class employee and department classes and vice-versa. Slicing Criterion (Department, D-id) Therefore static slices are conservative and contain more statements than necessary. A static program slice S consists of all statements in program P that may affect the value of variable v at some point p. The slice is defined for a slicing criterion- C = (x, V) (1) Where x is a class and V is a subset of variables in class. A static slice includes all the statements that affect variable v for a set of all possible inputs at the point of interest ( at the statement x). Class Level Slice: A class level slicing criterion is a triple C = (P, C, Vc) (2) Where C is a class in certain modular P, and Vc is variables set define or used in C. Class-level slice is a set which consist of all classes affecting and affected by the value of variables set Vc. The hierarchical slice based object oriented coupling measurement. In this paper, established a measuring coupling framework based on hierarchical slice model [7]. 4. CALCULATION OF SLICE BASED COUPLING OF CLASS DEPENDENCY GRAPH A coupling measure between classes, which class directly coupled with other class, this section explains slice-based coupling metrics of the class dependency graph. In this research paper we are the measure coupling on the basis of dependencies of attributes and its methods. Coupling is a measure of information flow between two classes. A metrics used show as below: Flow P (C1, C2) = Slice (P, c2, V c2) N (c1) / N (C2) Flow P (c1, c2) implies that information flow from class c1 to class c2 in a modular P, employee information modular. Flow P (C2, C1) = Slice (P, c1, V c1) N (c2) / N (C1) Flow P (c2, c1) implies that information flow from class c2 to class c1 in a modular P, employee information modular. Figure 3: Slice Based CDG of Employee Information Coupling (Employee, Department) = 1/ 8 = 0.1 Coupling (Department, Employee) = 0 5. CALCULATION OF SLICE BASED COHESION OF CLASS DEPENDENCY GRAPH Slice-based cohesion metrics and details the formulae used in their calculation. Firstly a class diagram covert into dependency graph and identified the attributes and method and its relationship. A slice apply on the basis of program slicing criterion and then calculate the cohesion on the basis of cohesion metrics Worked examples, illustrating the calculation of slice-based cohesion metrics for a class dependency graph. There are two slices in Figure 4, 5. Where Dep_D(n) = Dep* (n) / Nc Dep* (n): Consist of all attributes or method that n directly- indirectly potentially depends. Nc is the total number of nodes of class [2]. DRC(C) = 1/Nc Σ Dep_ D(n) DRC: A Dependence Relationship Based Cohesion Measure for Classes. Slicing criterion (Employee, e-id)

5 Determine Cohesion and Coupling for Class Diagram Through Slicing Techniques 23 Figure 4: Slice Based CDG of Employee Class A cohesion measure figure wise. Table 1 Cohesion of Employee Dependency Graph Node Dep*(n) Dep_D(n) Employee-id {Set e-id, First Name, 0.87 Last Name, , Set F N, Set L N, Set e-m} First Name {Set e-id, Last Name, 0.62 Set F N, Set L N, e-id} Last name {Set e-id, First Name, , Set F N, Set L N,} {Set e-id, First Name, 0.87 Set F N, Set L N, Set e-m, e-id, Last Name} Set Employee-id {e-m, First Name, , Set F N, Set L N, Set e-m, e-id} Set First Name {Set e-id, First Name, , L N, Set L N, Set e-m, e-id } Set Last name {Set e-id, First Name, , Set F N, L N, Set e-m, e-id} Set {Set e-id, First Name, , Set F N, Set L N, L N, e-id} Now total cohesion of the employee class DRC (C) = 1/Nc Σ Dep_D(n) = /8 = 6.46/8= 0.81 Slicing Criterion (Department, d-id) Table 2 Cohesion of Department Dependency Graph Node Dep*(n) Dep_D(n) D-id {Set d-id, Name, Set 0.87 Name, City, Set City, State, Set S} Name {Set d-id, Name, D-id, 0.87 City, Set City, State, Set S} City {State, Set S, Set City,} 0.37 State {Set d-id, Name, 0.87 Set Name, City, Set City, D-id, Set S,} Set D-id {d-id, Name, Set Name, 0.87 City, Set City, State, Set S} Set Name {Set d-id, Name, 0.87 D-id, City, Set City, State, Set S} Set City {City, State, Set S} 0.37 Set State {Set d-id, Name, 0.87 Set Name, City, Set City, State, d-id} Now total cohesion of the company class DRC(C) = 1/Nc Σ Dep_D(n) = = 5.96/ 8 = 0.74 Figure 5 6. PERFORMANCE ANALYSIS The performance analysis is used to correlate the data. Pearson s linear correlation is used to quantify the relation between metrics. Such correlations measure linear associations between variables. The output is a correlation coefficient, reported as the value R, and the coefficients of a linear model [17]. The statistical significance of R can be belong.

6 24 Akhilesh Kumar & Sunint Kaur Khalsa If R like between , then show strong association. If R like between , then show moderate association Otherwise show that weak or no association. If R gives the negative value indicates an inverse correlation. In our employee information example of coupling (In section IV) we found results R equal to 0.1, this result show that low coupling it means this is better for good software design system. The example of employee information for slicing in (In section V) we found results R equal to 0.81, this result indicate the high cohesion it means this is better for good software design system. So finally these results show the more reliable for software system. 7. CONCLUSION Cohesion and Coupling are an important factor in term of software design. Coupling indicates the degree of interdependence among the component of a software system.this paper, showed a class dependence relationships based coupling and cohesion measure for classes based on dependence relationships. The relationships among the members of a class are hence well characterized and can objectively evaluate the cohesiveness of a class. Proposed new techniques show the class dependency graph, dependency between attribute-attribute, attribute-method and method-method. The coupling measures focused only on the dependencies between classes on the basis of dependencies of attributes and its methods, (for example friendship between classes, specialization, and aggregation). Our calculating result indicate that the propose scheme significantly low coupling and high cohesion, so system are more reliable, efficient and credibility of class diagram is appropriate. Refrences [1] Lionel C. Briand, John W. Daly and Jürgen Wüst, A Unified Framework for Coupling Measurement in Object- Oriented Systems, IEEE Transactions on Software Engineering, Germany, Vol. 25, No. 1, [2] Yuming Zhou, Lijie Wen Jianmin and Wang Yujian Chen, A Dependence Relationships Based Cohesion Measure for Classes, IEEE 10 th Asia-Pacific Software Engineering Conference (APSEC) Department of Computer Science & Engineering, Southeast University, Nanjing, [3] Erik Arishol, Dynamic Coupling Measures for Object- Oriented Software, Proceedings of the Eighth IEEE Symposium on Software Metrics (METRICS-02), [4] Denys Poshyvanyk, and Andrian Marcus, The Conceptual Coupling Metrics for Object-Oriented Systems, IEEE International Conference on Software Maintenance (ICSM 06) Department of Computer Science Wayne State University Detroit Michigan, [5] Lionel Briand, Prem Devanbu and Walcelio Melo, An Investigation into Coupling Measures for C++, Proc. of the 19th International Conference on Software Engeneering, pp , May [6] James M. Bieman and Byung-Kyoo Kang, Measuring Design-Level Cohesion, IEEE Transactions on Software Engineering, Vol. 24, No. 2, Feb [7] Bixin li, A Hierarchical Slice Based Framework Object Oriented Coupling Measurement, Turku Centre of Computer Science, TUCS, Technical Report, [8] Timothy M. Meyers and David Binkley, Slice-Based Cohesion Metrics and Software Intervention, Timothy M. Meyers and David Binkley Loyola College in Maryland Baltimore, Maryland, pp , [9] V. Krishnapriya and Dr. K. Ramar, Exploring the Difference between Object Oriented Class Inheritance and Interfaces Using Coupling Measures, International Conference on Advances in Computer Engineering, [10] L.C. Briand, Y. Labiche, and Y. Wang. An Investigation of Graph-based Class Integration Test Order Strategies. IEEE Transaction on Software Engineering, pp , [11] L. C. Briand, J. Feng and Y. Labiche, Using Genetic Algorithms and Coupling Measures to Devise Optimal Integration Test Orders, Proceedings of 14th International Conference in Software Engineering and Knowledge Engineering, pp , [12] A. Abdurazik and A. J. Offutt, Using Coupling-based Weights for the Class Integration and Test Order Problem, The Computer Journal, pp , [13] P. Green, Lane, Rainer, Scholz, An Introduction to Slice- Based Cohesion and Coupling Metrics, Technical Report No. 488, University of Hertfordshire, School of Computer Science, [14] Timothy M. Meyers and David Binkley, Slice-Based Cohesion Metrics and Software Intervention, Loyola College in Maryland Baltimore, Maryland, pp [15] Zhenqiang Chen,Yuming Zhou and Baowen Xu, A Novel Approach to Measuring Class Cohesion Based on Dependence Analysis, IEEE International Conference on Software Maintenance (ICSM. 02), pp , [16] Jaiprakash T. Lallchandani and R. Mall, Static Slicing of UML Architectural Models, UML Architectural Models, in Journal of Object Technology, Vol. 8, pp , Jan-Feb [17] Timothy M. Meyers and David Binkley, An Empirical Study of Slice-Based Cohesion and Coupling Metrics, ACM Transactions on Software Engineering and Methodology (TOSEM), Dec