Abstraction and Abstract Data Types

Steven J. Zeil

Old Dominion University, Dept. of Computer Science

Table of Contents

1. Abstraction
1.1. Procedural Abstraction
1.2. Data Abstraction
2. Abstract Data Types
2.1. ADTs as contracts
2.2. ADT Implementations
2.3. Where Do ADTs Come From?

If we were to look at a program that is actually large enough to require a full team of programmers to implement it, you would probably not be surprised to find that it would not be organized as a single, large, monolithic unit, but instead as a large number of cooperating functions. You already know how to design and write functions in C++ . What may, however, come as a bit of a surprise if you have not worked much with programs that size is that even these functions will be further organized into higher level structure.

I've tried to illustrate that structure in the diagram that you see here. At the top we have the main application program, for example, a spell checker. The code that occurs at this level is very specific to this application and is the main thing that differentiates a spell checker from, let's say, a spreadsheet. On the other hand, at the very bottom of the hierarchy, we have all the basic primitive data types and operations, such as the int type, the char type, addition, subtraction, and so on, that are provided by our programming language, C++. These primitive operations may very well show up in almost any kind of program.

In between those, we have all the things that we can build on top of the language primitives on our way working up towards our application program. Just above the language primitives we have the basic data structures, structures like linked lists or trees. We're going to spend a lot of time the semester looking at these kinds of structures - you may already be familiar with some of them. They are certainly very important. And yet, if we stopped at that level, we would wind up "building to order" for every application. As we move from one application to another we would often find ourselves doing the same kinds of coding, over and over again.

What's wrong with that? Most companies, and therefore most programmers, do not move from one application to a wildly different application on the next project. Programmers who been working on accounts receivable are unlikely to start writing compilers the next week, and programmers who have been writing compilers are not going to be writing control software for jet aircraft the week after. Instead, programmers are likely to remain within a general application domain. The people who are currently working on our spell checker may very well be assigned to work on a grammar checker next month, or on some other text processing tool. That means that any special support we can design for dealing with text, words, sentences, or other concepts natural to this application to make may prove valuable in the long run because we can share that work over the course of several projects.

And so, on top of the basic data structures, we expect to find layers of reusable libraries. Just above the basic data structures, the libraries are likely to provide fairly general purpose structures, such as support for look-up tables, user interfaces, and the like. As we move up in the hierarchy, the libraries become more specialized to the application domain in which we're working. Just below the application level, we will find support for concepts that are very close to the spell checker, such as "words", "misspellings", and "corrections".

The libraries that make up all but the topmost layer of this diagram may contain individual functions or groups of functions organized as Abstract Data Types. In this lesson, we'll review the idea of Abstract Data Types and how to use C++ classes to implement them. None of the material in this lesson should be entirely new to you - all of it is covered in CS 250.

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