CSC/ECE 517 Fall 2007/wiki2 7 b2

Assignment 2 - Topic 7 - Cohesion and Coupling

Cohesion and coupling are concepts that are reasonably easy to understand, but nonetheless, it is challenging to find good examples that are succinct. Browse the hundreds of Web pages that attempt to explain these concepts, picking your favorite examples. Many of these pages mention related concepts; list some of them and explain how they relate to cohesion and coupling. Be sure to mention the metrics that can be used to measure how well a program conforms to the principles of high cohesion and low coupling.

The Concepts

The object-oriented paradigm, the virtuals of readability, maintainability, extensibility, reusability are realized through modularity, which aims at decomposing a complex problem into smaller pieces called modules (e.g. class). A module can be understood as a contiguous program statements, bounded together, having a set of its own variable names and an aggretate identifer.

Well designed object-oriented programs defines proper logic and operational boundaries among different modules, through which many aforementioned desiable properties can be achieved. The degree of interaction within one module was quantified and called module cohesion by Myers in 1978 and the degree of interaction between two different modules was called module coupling. It is considered as good traits when a program has high level cohesion while a low level coupling.

Cohesion

Types of Cohesion From worst to best, the types of cohesion are listed below:

• Coincidental Cohesion (worst)
• Logical Cohesion
• Temporal Cohesion
• Procedual Cohesion
• Communicational Cohesion
• Sequential Cohesion
• Functional Cohesion
• Informational Cohension (best)

Coincidental Cohesion (worst)

Coincidental cohesion means the module are grouped randomly, without significant relationshiop. Example,

module miscellaneous
  sin();
  read();
  sendsignal();
  ...
end

Logical Cohesion

A logical cohesion module is one whose elements are somewhat in the same category. Example,

module IOfunction
  printf();
  read_A;
  write_B;
end

Temporal Cohesion

The elements of the module are related in time, a good example is program initialization and termination.

module init
  timer_A=0;
  couter_B=0;
  ...
end

Procedual Cohesion

A procedual cohesion module is one that its elements are involved in different activities but executed in a specific order. Example,

module tableupdate
  read_A();
  wirte_B();
  ...
end

Communicational Cohesion

A communicationally module is one whose elements are not related but use the same input or output data.

module data_A
  task1_read_A();
  task2_read_A();
  task1_write_A();
  ...
end

Sequential Cohesion

A sequentially cohesive module is that one of its elements's output data happens to be input data to another one.

module data_update
  A.update();
  B.update(A);
  ...
end

Functional Cohesion

A sequentially cohesive module is one whose elements contribute to one task, or one problem.

module task
  task.init;
  task.compute;
  task.io;
  ...
end

Informational Cohension (best)

A informationally cohensive module is a "object-oriented" module. It contains complex data structure and all its elements contribute to the object.

module class_A
  A.init;
  A.read;
  A.write;
  ...
end

Cohesion Metrics

A way to decide the type of cohension.

Coupling

Coupling is the degree of interaction between two modules. The levels of couplingis listed below.

Types of Coupling

From worst to best:

• Content Coupling (worst)
• Common Coupling
• Control Coupling
• Stamp Coupling
• Data Coupling (best)

Content Coupling (worst)

Two modules are said to be content coupled when one module have direct access or reference to the contents of the other one. Content coupling suffers a great disadvantage. It is simply impossible to reuse content coupled modules because any change to one of the modules would require change to another one. one example would be

Common Coupling

Two modules are said to be common coupled if both have access to the same global data. Common coupling is undesirable for several reasons. First, if the commonly referred global variale is changed, then every common coupled module has to be changed as well. This greatlly decreases the code maintainability. Second, because more than one module has the potential to modify the value of a global variable, when an error occurs, it is difficult to identify the cause due to very minimal readability. Third, data access control becomes very difficult with common coupled modules. However, common coupling could be a solution when a number of modules have to access a set of attributes defined as global variables.

Control Coupling

Two modules are said to be control coupled when one module passes a control element to the other module.

example: There are two modules m1 and m2. When m1 passes a string "mission accomplished" to m2, without commanding m2 to do anything, then m1 simply passes data to m1. However, if m1 pass "mission accomplished" followed by commanding m2 to signal correspondingly, then they are control coupled. Control coupling destroys the independence of modules and therefore suffers the disadvantage of decreased reusability.

Stamp Coupling

If a data structure (e.g. a struct in C,) is passed as an argument from one module to the other, but instead of using all data contained in the data structure, the receiver of the argument only uses a part of it, then these two modules are said to be stampl coupled.

example: calculate_witholding( employee_record )

In this example, calculate_witholding will probably need employee salary and id to carry out the computation, however, other data such as address, contact of employee_record are not likely to be needed.

Because a module receives more information than it really needs, the difficulty of data access control suffered by common coupled modules also troubles stamp coupled modules. If arguments passed to a module can be reduced to those necessary ones, it not only increases the readability of the code but also improves the likelyhood of it being reused.

Data Coupling

When two modules pass arguments that are either simple arguments or a data structure in which all elements are used by the called module, then they are said to be data coupled.

example:

• display_time_of arrival( flight_number )
• compute_product( a, b, result )

Data coupling makes the maintainance easier and do not suffer problems introduced in other types of coupling.

Related Concepts

Coupling and Cohesion Metrics

Cohesion Definition

Cohesion is the degree to which the elements in a design unit (package, class etc.) are logically related, or "belong together". As such, cohesion is a semantic concept.

Cohesion metrics have been proposed which attempt to approximate this semantic concept using syntactical criteria. Such metrics quantify the connectivity (coupling) between elements of the design unit: the higher the connectivity between elements, the higher the cohesion.

Cohesion metrics often are normalized to have a notion of minimum and maximum cohesion, usually expressed on a scale from 0 to 1. Minimum cohesion (0) is assumed when the elements are entirely unconnected, maximum cohesion (1) is assumed when each element is connected to every other element.

Not normalized metrics are based on counts of connections between design elements in a unit (e.g., method calls within a class). As such, not normalized metrics are conceptually similar to complexity metrics.

Impact on quality

A low cohesive design element has been assigned many unrelated responsibilities. Consequently, the design element is more difficult to understand and therefore also harder to maintain and reuse. Design elements with low cohesion should be considered for refactoring, for instance, by extracting parts of the functionality to separate classes with clearly defined responsibilities.

Empirical results

In practice, cohesion metrics are only of limited usefulness:

   * Not normalized cohesion metrics often are strongly related to size metrics. This makes sense since, as discussed, large classes or packages may in fact suffer from low cohesion. Such cohesion metrics then are, of course, good quality indicators, but they are redundant with size metrics - they provide no new information about the element.
   * Normalized cohesion metrics do not consistently have a bearing on system quality. I.e., we cannot conclude from a high or low cohesion value that a class is, e.g., more or less fault-prone. Either, the theoretical negative impact of low cohesion on system quality is not always that critical in practice, or, the cohesion metrics simply fail to identify design elements with unrelated responsibilities.

Coupling Definition

Coupling is the degree to which the elements in a design are connected.

Impact on quality

Coupling connections cause dependencies between design elements, which, in turn, have an impact on system qualities such as maintainability (a modification of a design element may require modifications to its connected elements) or testability (a fault in one design element may cause a failure in a completely different, connected element). Thus, a common design principle is to minimize coupling.

Most coupling dependencies are directed - the coupling usually defines a client-supplier relationship between the design elements. Therefore, it is useful to distinguish import coupling ("using") and export coupling ("used"), which we discuss in the following.

Import coupling

Import coupling measures the degree to which an element has knowledge of, uses, or depends on other design elements. High import coupling can have the following effects:

   * Decreased maintainability: changes to the supplier may necessitate follow-up changes (ripple effects) to the client.
     The stability of the supplier is a factor to consider here. High coupling to elements that are not likely to change is less harmful than coupling to variation points.
   * Decreased understandability, increased fault-proneness: elements with high import coupling operate in large context, developers need to know all the services the element relies on, and how to use them.
   * Decreased reusability: To reuse a class or package with high import coupling in a new context, all the required services must also be made available in the new context. 

Export coupling

Export coupling measures the degree to which an element is used by, depended upon, by other design elements. High export coupling is often observed for general utility classes (e.g., for string handling or logging services) that are used pervasively across all layers of the system. Thus, high export coupling is not necessarily indicative of bad design.

Again, an important issue to consider here is stability. High export coupling elements that are likely to change in the future can have a large impact on the system if the change affects the interface. Therefore, high export classes should be reviewed for anticipated changes, to ensure that these changes can implemented with minimal impact.

Empirical results

Coupling metrics have consistently been found to be good indicators of fault-proneness. It seems worthwhile to investigate different dimensions of coupling: import and export coupling, different coupling mechanisms, distinguishing coupling to COTS libraries and application-specific classes/packages. Coupling metrics are suitable to identify design elements with high fault density. Therefore, coupling metrics greatly help to identify small parts of a design that contain a large number of faults.

Reference

• E. Yourdon, Modern Structured Analysis, Yourdon Press, Englewood Cliffs, NJ, 1989.
• Stephen R. Schach, Object-oriented & classical Software Engineering, McGraw-Hill, NY, NY, 2005

External Links

• Cohesion - An wikipedia article about cohesion
• Coupling - An wikipedia article about coupling
• [1] - Design Concepts: Cohesion vs. Coupling
• [2] - Coupling and Cohesion
• [3] - A Definition of Coupling and Cohesion
• [4] - What makes a module a good module?
• [5] - Design for change
• [6] - Software Quality and Coupling and Cohesion