The progress of abstraction

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All
programming languages provide abstractions. It can be argued that the
complexity of the problems you can solve is directly related to the kind and
quality of abstraction.
By “kind” I mean: what is it that you are abstracting? Assembly
language is a small abstraction of the underlying machine. Many so-called
“imperative” languages that followed (such as FORTRAN, BASIC, and
C) were abstractions of assembly language. These languages are big improvements
over assembly language, but their primary abstraction still requires you to
think in terms of the structure of the computer rather than the structure of
the problem you are trying to solve. The programmer must establish the
association between the machine model (in the “solution space”) and
the model of the problem that is actually being solved (in the “problem
space”). The effort required to perform this mapping, and the fact that
it is extrinsic to the programming language, produces programs that are
difficult to write and expensive to maintain, and as a side effect created the
entire “programming methods” industry.

The
alternative to modeling the machine is to model the problem you’re trying
to solve. Early languages such as LISP and APL chose particular views of the
world (“all problems are ultimately lists” or “all problems
are algorithmic”). PROLOG casts all problems into chains of decisions.
Languages have been created for constraint-based programming and for
programming exclusively by manipulating graphical symbols. (The latter proved
to be too restrictive.) Each of these approaches is a good solution to the
particular class of problem they’re designed to solve, but when you step
outside of that domain they become awkward.

The
object-oriented approach takes a step farther by providing tools for the
programmer to represent elements in the
problem
space. This representation is general enough that the programmer is not
constrained to any particular type of problem. We refer to the elements in the
problem space and their representations in the solution space as

“objects.” (Of course, you will also need other objects that
don’t have problem-space analogs.) The idea is that the program is
allowed to adapt itself to the lingo of the problem by adding new types of
objects, so when you read the code describing the solution, you’re
reading words that also express the problem. This is a more flexible and
powerful language abstraction than what we’ve had before. Thus OOP allows
you to describe the problem in terms of the problem, rather than in the terms
of the solution. There’s still a connection back to the computer, though.
Each object looks quite a bit like a little computer; it has a state, and it
has operations you can ask it to perform. However, this doesn’t seem like
such a bad analogy to objects in the real world; they all have characteristics
and behaviors.

  1. Everything
    is an object.

    Think of an object as a fancy variable; it stores data, but you can also ask it
    to perform operations on itself by making requests. In theory, you can take any
    conceptual component in the problem you’re trying to solve (dogs,
    buildings, services, etc.) and represent it as an object in your program.
  2. A
    program is a bunch of objects telling each other what to do by sending messages
    .
    To make a request of an object, you “send a message” to that
    object. More concretely, you can think of a message as a request to call a
    function that belongs to a particular object.
  3. Each
    object has its own memory made up of other objects
    .
    Or, you make a new kind of object by making a package containing existing
    objects. Thus, you can build up complexity in a program while hiding it behind
    the simplicity of objects.
  4. Every
    object has a type
    .
    Using the parlance, each object is an
    instance
    of a
    class,
    where “class” is synonymous with “type.” The most
    important distinguishing characteristic of a class is “what messages can
    you send to it?”
  5. All
    objects of a particular type can receive the same messages
    .
    This is actually a very loaded statement, as you will see later. Because an
    object of type circle is also an object of type shape, a circle is guaranteed
    to receive shape messages. This means you can write code that talks to shapes
    and automatically handle anything that fits the description of a shape. This
    substitutability
    is one of the most powerful concepts in OOP.

[2]
See
Multiparadigm
Programming in Leda

by Timothy Budd (Addison-Wesley 1995).

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