Software Engineering-Data Structure


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. 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 ndimensional 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. Depending on the level of design detail, the internal workings of a stack may or may not be specified.

Software Procedure

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, including sequence of events, exact decision points, repetitive operations, and even data organization and structure.

There is, of course, a relationship between structure and procedure. The processing indicated for each module must include a reference to all modules subordinate to the module being described. That is, a procedural representation of software is ayered as illustrated in figure.

Information Hiding

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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