A set of fundamental software design concepts has evolved over the past four decades.Although the degree of interest in each concept has varied over the years, each has stood the test of time. Each provides the software designer with a foundation from which more sophisticated design methods can be applied. Each helps the software engineer to answer the following questions:
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When we consider a modular solution to any problem, many levels of abstraction can be posed. At the highest level of abstraction, a solution is stated in broad terms using the language of the problem environment. At lower levels of abstraction, a more procedural orientation is taken. Problem-oriented terminology is coupled with implementation-oriented terminology in an effort to state a solution. Finally, at the lowest level of abstraction, the solution is stated in a manner that can be directly implemented.
definition:The psychological notion of “abstraction” permits one to concentrate on a problem at some level of generalization without regard to irrelevant low level details; use of abstraction also permits one to work with concepts and terms that are familiar in the problem environment without having to transform them to an unfamiliar structure .Each step in the software process is a refinement in the level of abstraction of the software solution. During system engineering, software is allocated as an element of a computer-based system. During software requirements analysis, the software solution is stated in terms “that are familiar in the problem environment.” As we move through the design process, the level of abstraction is reduced. Finally, the lowest level of abstraction is reached when source code is generated.As we move through different levels of abstraction, we work to create procedural and data abstractions. A procedural abstraction is a named sequence of instructions that has a specific and limited function. An example of a procedural abstraction would be the word open for a door. Open implies a long sequence of procedural steps (e.g.,walk to the door, reach out and grasp knob, turn knob and pull door, step away from
moving door, etc.)
A data abstraction is a named collection of data that describes a data object. In the context of the procedural abstraction open, we can define a data abstraction called door. Like any data object, the data abstraction for door would encompass a set of attributes that describe the door (e.g., door type, swing direction, opening mechanism, weight, dimensions). It follows that the procedural abstraction open would make use of information contained in the attributes of the data abstraction door.Many modern programming languages provide mechanisms for creating abstract data types. For example, the Ada package is a programming language mechanism that provides support for both data and procedural abstraction. The original abstract data type is used as a template or generic data structure from which other data structures can be instantiated.Control abstraction is the third form of abstraction used in software design. Like procedural and data abstraction, control abstraction implies a program control mechanism without specifying internal details.
Stepwise refinement is a top-down design strategy . A program is developed by successively refining levels of procedural detail.Hierarchy is developed by decomposing a macroscopic statement of function (a procedural abstraction) in a stepwise fashion until programming language statements are reached. In each step (of the refinement), one or several instructions of the given program are decomposed into more detailed instructions. This successive decomposition or refinement of specifications terminates when all instructions are expressed in terms of any underlying computer
or programming language .As tasks are refined, so the data may have to be refined, decomposed, or structured, and it is natural to refine the program and the data specifications in parallel.
Every refinement step implies some design decisions. It is important that the programmer be aware of the underlying criteria (for design decisions) and of the existence of alternative solutions .The process of program refinement is analogous to the process of refinement and partitioning that is used during requirements analysis. The difference is in the level of implementation detail that is considered, not the approach.
Refinement is actually a process of elaboration. We begin with a statement of function (or description of information) that is defined at a high level of abstraction. That is, the statement describes function or information conceptually but provides no information about the internal workings of the function or the internal structure of the information. Refinement causes the designer to elaborate on the original statement,
providing more and more detail as each successive refinement (elaboration) occurs.Abstraction and refinement are complementary concepts. Abstraction enables a designer to specify procedure and data and yet suppress low-level details. Refinement helps the designer to reveal low-level details as design progresses. Both concepts aid the designer in creating a complete design model as the design evolves.
The concept of modularity in computer software has been espoused for almost five decades. Software architecture embodies modularity; that is, software is divided into separately named and addressable components, often called modules , that are integrated to satisfy problem requirements.
It has been stated that “modularity is the single attribute of software that allows a program to be intellectually manageable”. Monolithic software (i.e., a large program composed of a single module) cannot be easily grasped by a reader. The number of control paths, span of reference, number of variables, and overall complexity would make understanding close to impossible. Another important question arises when modularity is considered. How do we define an appropriate module of a given size? The answer lies in the method(s) used to define modules within a system. five criteria that enable us to evaluate a design method with respect to its ability to define an effective modular system:
Modular decomposability. If a design method provides a systematic
Mechanism for decomposing the problem into sub problems, it will reduce the complexity of the overall problem, thereby achieving an effective modular solution.
Modular composability. If a design method enables existing (reusable)
Design components to be assembled into a new system, it will yield a modular solution that does not reinvent the wheel.
Modular understandability. If a module can be understood as a standalone unit (without reference to other modules), it will be easier to build and easier to change.
Modular continuity. If small changes to the system requirements result in changes to individual modules, rather than system wide changes, the impact of change-induced side effects will be minimized.
Modular protection. If an aberrant condition occurs within a module and
Its effects are constrained within that module, the impact of error-induced
Side effects will be minimized. Finally, it is important to note that a system may be designed modularly, even if its implementation must be “monolithic.” There are situations (e.g., real-time software, embedded software) in which relatively minimal speed and memory overhead introduced by subprograms (i.e., subroutines, procedures) is unacceptable. In such situations, software can and should be designed with modularity as an overriding philosophy. Code may be developed “in-line.” Although the program source code may not look modular at first glance, the philosophy has been maintained and the program will provide the benefits of a modular system.
Software architecture alludes to “the overall structure of the software and the ways in which that structure provides conceptual integrity for a system”. In its simplest form, architecture is the hierarchical structure of program components (modules), the manner in which these components interact and the structure of data that are used by the components. In a broader sense, however, components can be generalized to represent major system elements and their interactions. One goal of software design is to derive an architectural rendering of a system. This rendering serves as a framework from which more detailed design activities are conducted. A set of architectural patterns enable a software engineer to reuse design level concepts.
Structural properties. This aspect of the architectural design representation defines the components of a system (e.g., modules, objects, filters) and the manner in which those components are packaged and interact with one another. For example, objects are packaged to encapsulate both data and the processing that manipulates the data and interact via the invocation of methods.
Extra-functional properties. The architectural design description should address how the design architecture achieves requirements for performance, capacity, reliability, security, adaptability, and other system characteristics.
Families of related systems. The architectural design should draw upon repeatable patterns that are commonly encountered in the design of families of similar systems. In essence,the design should have the ability to reuse architectural building blocks.Given the specification of these properties, the architectural design can be represented using one or more of a number of different models . Structural models represent architecture as an organized collection of program components.Framework models increase the level of design abstraction by attempting to identify repeatable architectural design frameworks (patterns) that are encountered in similar types of applications. Dynamic models address the behavioral aspects of the program architecture, indicating how the structure or system configuration may change as a function of external events. Process models focus on the design of the business or technical process that the system must accommodate. Finally, functional models can be used to represent the functional hierarchy of a system.A number of different architectural description languages (ADLs) have been developed to represent these models [SHA95b]. Although many different ADLs have been proposed, the majority provide mechanisms for describing system components and the manner in which they are connected to one another.
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Control hierarchy, also called program structure, represents the organization of program components (modules) and implies a hierarchy of control. It does not represent procedural aspects of software such as sequence of processes, occurrence or order of decisions, or repetition of operations; nor is it necessarily applicable to all architectural styles.Different notations are used to represent control hierarchy for those architectural styles that are amenable to this representation. Depth and width provide an indication of the number of levels of control and overall span of control, respectively. Fan-out is a measure of the number of modules that are directly controlled by another module. Fan-in indicates how many modules directly control a given module.The control relationship among modules is expressed in the following way: A module
that controls another module is said to be superordinate to it, and conversely, a module controlled by another is said to be subordinate to the controller .The control hierarchy also represents two subtly different characteristics of the software architecture: visibility and connectivity. Visibility indicates the set of program components that may be invoked or used as data by a given component, even when this is accomplished indirectly. For example, a module in an object-oriented system may have access to a wide array of data objects that it has inherited, but makes use
of only a small number of these data objects. All of the objects are visible to the module.
Connectivity indicates the set of components that are directly invoked or used as data by a given component. For example, a module that directly causes another module to begin execution is connected to it.
If the architectural style of a system is hierarchical, the program structure can be partitioned both horizontally and vertically. Referring to Figure 13.4a, horizontal partitioning defines separate branches of the modular hierarchy for each major program function. Control modules, represented in a darker shade are used to coordinate communication between and execution of the functions. The simplest approach to horizontal partitioning defines three partitions-input, data transformation (often called processing) and output. Partitioning the architecture horizontally provides a number of distinct benefits:
â€¢ Software that is easier to test
â€¢ Software that is easier to maintain
â€¢ Propagation of fewer side effects
â€¢ Software that is easier to extend
Because major functions are decoupled from one another, change tends to be less complex and extensions to the system (a common occurrence) tend to be easier to accomplish without side effects. On the negative side, horizontal partitioning often causes more data to be passed across module interfaces and can complicate the overall control of program flow (if processing requires rapid movement from one function to another).
Vertical partitioning, often called factoring, suggests that control and
Modules should perform control functions and do little actual processing work. Modules that reside low in the structure should be the workers, performing all input, computation, and output tasks. The nature of change in program structures justifies the need for vertical partitioning it can be seen that a change in a control module (high in the structure) will have a higher probability of propagating side effects to modules that are subordinate to it. A change to a worker module, given its low level in the structure, is less likely to cause the propagation of side effects. In general,
Changes to computer programs revolve around changes to input, computation or transformation, and output. The overall control structure of the program (i.e., its basic behavior is far less likely to change). For this reason vertically partitioned structures are less likely to be susceptible to side effects when changes are made and will therefore
be more maintainable-a key quality factor.
Data structure is a representation of the logical relationship among individual elements of data. Because the structure of information will invariably affect the final procedural design, data structure is as important as program structure to the representation of software architecture. Data structure dictates the organization, methods of access, degree of associativity, and processing alternatives for information. However, it is important to understand the classic methods available for organizing information and the concepts that underlie information hierarchies.The organization and complexity of a data structure are limited only by the ingenuity of the designer. There are, however, a limited number of classic data structures that form the building blocks for more sophisticated structures. A scalar item is the simplest of all data structures. As its name implies, a scalar item represents a single element of information that may be addressed by an identifier; that is, access may be achieved by specifying a single address in memory. The size and format of a scalar item may vary within bounds that are dictated by a programming language. For example, a scalar item may be a logical entity one bit long,
an integer or floating point number that is 8 to 64 bits long, or a character string that is hundreds or thousands of bytes long. When scalar items are organized as a list or contiguous group, a sequential vector is formed. Vectors are the most common of all data structures and open the door to
Variable indexing of information. When the sequential vector is extended to two, three, and ultimately, an arbitrary number of dimensions, an n-dimensional space is created. The most common n-dimensional
space is the two-dimensional matrix. In many programming languages, an n dimensional space is called an array.Items, vectors, and spaces may be organized in a variety of formats. A linked list is a data structure that organizes noncontiguous scalar items, vectors, or spaces in a manner (called nodes) that enables them to be processed as a list. Each node contains the appropriate data organization (e.g., a vector) and one or more pointers that indicate the address in storage of the next node in the list. Nodes may be added at any point in the list by redefining pointers to accommodate the new list entry. Other data structures incorporate or are constructed using the fundamental data structures just described. For example, a hierarchical data structure is implemented using multilinked lists that contain scalar items, vectors, and possibly, n-dimensional spaces. A hierarchical structure is commonly encountered in applications that require information categorization and associativity.It is important to note that data structures, like program structure, can be represented at different levels of abstraction. For example, a stack is a conceptual model of a data structure that can be implemented as a vector or a linked list. Program structure defines control hierarchy without regard to the sequence of processing and decisions. Software procedure focuses on the processing details of each module individually. Procedure must provide a precise specification of processing,
The concept of modularity leads every software designer to a fundamental question:” How do we decompose a software solution to obtain the best set of modules?”
The principle of information hiding suggests that modules be “characterized by design decisions that (each) hides from all others.” In other words, modules should be specified and designed so that information (procedure and data) contained within a module is inaccessible to other modules that have no need for such information. Hiding implies that effective modularity can be achieved by defining a set of independent modules that communicate with one another only that information necessary to achieve software function. Abstraction helps to define the procedural (or informational) entities that make up the software. Hiding defines and enforces access constraints to both procedural detail within a module and any local data structure used by the module.The use of information hiding as a design criterion for modular systems provides the greatest benefits when modifications are required during testing and later, during software maintenance. Because most data and procedure are hidden from other parts of the software, inadvertent errors introduced during modification are less likely to propagate to other locations within the software.
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