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Authors: Harold Abelson and Gerald Jay Sussman with Julie Sussman

Structure and Interpretation of Computer Programs (47 page)

BOOK: Structure and Interpretation of Computer Programs
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The trouble here is that substitution is based ultimately on the
notion that the symbols in our language are essentially names for
values. But as soon as we introduce
set!
and the idea that the
value of a variable can change, a variable can no longer be simply a
name. Now a variable somehow refers to a place where a value can be
stored, and the value stored at this place can change. In
section 
3.2
we will see how environments play this role of “place” in our
computational model.

Sameness and change

The issue surfacing here is more profound than the mere breakdown of a
particular model of computation. As soon as we introduce change into
our computational models, many notions that were previously
straightforward become problematical. Consider the concept of two
things being “the same.”

Suppose we call
make-decrementer
twice with the same argument to
create two procedures:

(define D1 (make-decrementer 25))
(define D2 (make-decrementer 25))

Are
D1
and
D2
the same? An acceptable answer is yes,
because
D1
and
D2
have the same computational
behavior – each is a procedure that subtracts its input from 25. In
fact,
D1
could be substituted for
D2
in any computation
without changing the result.

Contrast this with making two calls to
make-simplified-withdraw
:

(define W1 (make-simplified-withdraw 25))
(define W2 (make-simplified-withdraw 25))

Are
W1
and
W2
the same? Surely not, because calls to
W1
and
W2
have distinct effects, as shown by the following
sequence of interactions:

(W1 20)
5
(W1 20)
- 15
(W2 20)
5

Even though
W1
and
W2
are “equal” in the sense that they
are both created by evaluating the same expression,
(make-simplified-withdraw 25)
, it is not true that
W1
could be
substituted for
W2
in any expression without changing the result
of evaluating the expression.

A language that supports the concept that “equals can be substituted
for equals” in an expresssion
without changing the value of the expression is said to be
referentially transparent
. Referential transparency is violated
when we include
set!
in our computer language. This makes it
tricky to determine when we can simplify expressions by substituting
equivalent expressions. Consequently, reasoning about programs that
use assignment becomes drastically more difficult.

Once we forgo referential transparency, the notion of what it means
for computational objects to be “the same” becomes difficult to
capture in a formal way. Indeed, the meaning of “same” in the real
world that our programs model is hardly clear in itself. In general,
we can determine that two apparently identical objects are indeed
“the same one” only by modifying one object and then observing
whether the other object has changed in the same way. But how can we
tell if an object has “changed” other than by observing the “same”
object twice and seeing whether some property of the object differs
from one observation to the next? Thus, we cannot determine
“change” without some
a priori
notion of “sameness,” and we
cannot determine sameness without observing the effects of change.

As an example of how this issue arises in programming, consider the
situation where Peter and Paul have a bank account with $100 in
it. There is a substantial difference between modeling this as

(define peter-acc (make-account 100))
(define paul-acc (make-account 100))

and modeling it as

(define peter-acc (make-account 100))
(define paul-acc peter-acc)

In the first situation, the two bank accounts are distinct.
Transactions made by Peter will not affect Paul's account, and vice
versa. In the second situation, however, we have defined
paul-acc
to be
the same thing
as
peter-acc
. In effect,
Peter and Paul now have a joint bank account, and if Peter makes a
withdrawal from
peter-acc
Paul will observe less money in
paul-acc
. These two similar but distinct situations can cause
confusion in building computational models. With the shared account,
in particular, it can be especially confusing that there is one object
(the bank account) that has two different names (
peter-acc
and
paul-acc
); if we are searching for all the places in our program
where
paul-acc
can be changed, we must remember to look also at
things that change
peter-acc
.
10

With reference to the above remarks on “sameness” and “change,”
observe that if Peter and Paul could only examine their bank balances,
and could not perform operations that changed the balance, then the
issue of whether the two accounts are distinct would be moot. In
general, so long as we never modify data objects, we can regard a
compound data object to be precisely the totality of its pieces. For
example, a rational number is determined by giving its numerator and
its denominator. But this view is no longer valid in the presence of
change, where a compound data object has an “identity” that is
something different from the pieces of which it is composed. A bank
account is still “the same” bank account even if we change the
balance by making a withdrawal; conversely, we could have two
different bank accounts with the same state information. This
complication is a consequence, not of our programming language, but of
our perception of a bank account as an object. We do not, for
example, ordinarily regard a rational number as a changeable object
with identity, such that we could change the numerator and still have
“the same” rational number.

Pitfalls of imperative programming

In contrast to functional programming, programming that makes
extensive use of assignment is known as
imperative
programming
. In addition to raising complications about
computational models, programs written in imperative style are
susceptible to bugs that cannot occur in functional programs. For
example, recall the iterative factorial program from
section 
1.2.1
:

(define (factorial n)
  (define (iter product counter)
    (if (> counter n)
        product
        (iter (* counter product)
              (+ counter 1))))
  (iter 1 1))

Instead of passing arguments in the internal iterative loop, we could
adopt a more imperative style by using explicit assignment
to update the values of the variables
product
and
counter
:

(define (factorial n)
  (let ((product 1)
        (counter 1))
    (define (iter)
      (if (> counter n)
          product
          (begin (set! product (* counter product))
                 (set! counter (+ counter 1))
                 (iter))))
    (iter)))

This does not change the results produced by the program, but it does
introduce a subtle trap. How do we decide the order of the assignments?
As it happens, the program is correct as written. But
writing the assignments in the opposite order

(set! counter (+ counter 1))
(set! product (* counter product))

would have produced a different, incorrect result.
In general, programming with assignment forces us to
carefully consider the relative orders of the assignments to make sure
that each statement is using the correct version of the variables that
have been changed. This issue simply does not arise in functional
programs.
11
The complexity of imperative programs becomes even worse if we
consider applications in which several processes execute concurrently. We
will return to this in section 
3.4
.
First, however, we will address the issue of providing a computational
model for expressions that involve assignment, and explore the uses of
objects with local state in designing simulations.

Exercise 3.7.
  
Consider the bank account objects created by
make-account
, with
the password modification described in
exercise 
3.3
. Suppose that our banking
system requires the ability to make joint accounts. Define a
procedure
make-joint
that accomplishes this.
Make-joint
should take three arguments. The first is a password-protected
account. The second argument must match the password with which the
account was defined in order for the
make-joint
operation to
proceed. The third argument is a new password.
Make-joint
is
to create an additional access to the original account using the new
password. For example, if
peter-acc
is a bank account with
password
open-sesame
, then

(define paul-acc
  (make-joint peter-acc 'open-sesame 'rosebud))

will allow one to make transactions on
peter-acc
using the name
paul-acc
and the password
rosebud
. You may wish to modify
your solution to exercise 
3.3
to accommodate
this new feature.

Exercise 3.8.
  
When we defined the evaluation model in
section 
1.1.3
, we said that the first step
in evaluating an expression is to evaluate its subexpressions. But we
never specified the order in which the subexpressions should be
evaluated (e.g., left to right or right to left). When we introduce
assignment, the order in which the arguments to a procedure are
evaluated can make a difference to the result. Define a simple
procedure
f
such that evaluating
(+ (f 0) (f 1))
will
return 0 if the arguments to
+
are evaluated from left to right
but will return 1 if the arguments are evaluated from right to left.

1
Actually,
this is not quite true. One exception was the
random-number generator
in section 
1.2.6
. Another exception involved the
operation/type tables we introduced in section 
2.4.3
,
where the values of two calls to
get
with the same arguments
depended on intervening calls to
put
.
On the other hand, until we introduce
assignment, we have no way to create such procedures ourselves.

2
The value of a
set!
expression is implementation-dependent.
Set!
should be used only for its effect, not for its value.

The name
set!
reflects a naming convention used in Scheme: Operations
that change the values of variables (or that change data structures,
as we will see in section 
3.3
) are given names that
end with an exclamation point. This is similar to the convention of
designating predicates by names that end with a question mark.

3
We have already used
begin
implicitly in our
programs, because in Scheme the body of a procedure can be a sequence
of expressions. Also, the <
consequent
> part of each clause in a
cond
expression can be a sequence of expressions rather than a
single expression.

4
In programming-language jargon, the variable
balance
is said to be
encapsulated
within the
new-withdraw
procedure. Encapsulation reflects the general
system-design principle known as the
hiding principle
: One can
make a system more modular and robust by protecting parts of the
system from each other; that is, by providing information access only
to those parts of the system that have a “need to know.”

5
In contrast with
new-withdraw
above, we do not
have to use
let
to make
balance
a local variable, since
formal parameters are already local. This will be clearer after the
discussion of the environment model of evaluation in section 
3.2
.
(See also exercise 
3.10
.)

6
One common way to implement
rand-update
is to use the rule that
x
is updated to
a
x
+
b
modulo
m
, where
a
,
b
, and
m
are appropriately chosen integers.
Chapter 3 of
Knuth 1981 includes an extensive discussion of techniques
for generating sequences of random numbers and establishing their
statistical properties. Notice that the
rand-update
procedure
computes a mathematical function: Given the same input twice, it
produces the same output. Therefore, the number sequence produced by
rand-update
certainly is not “random,” if by “random” we
insist that each number in the sequence is unrelated to the preceding
number. The relation between “real randomness” and so-called
pseudo-random
sequences, which are produced by well-determined
computations and yet have suitable statistical properties, is a
complex question involving difficult issues in mathematics and
philosophy.
Kolmogorov, Solomonoff, and Chaitin have made great
progress in clarifying these issues; a discussion can be found in
Chaitin 1975.

7
This theorem is due to E.
Cesàro. See
section 4.5.2 of
Knuth 1981 for a discussion and a proof.

8
MIT Scheme provides such a procedure. If
random
is given an exact
integer (as in section 
1.2.6
) it returns an exact integer,
but if it is given a decimal value (as in this exercise) it returns
a decimal value.

9
We don't substitute for
the occurrence of
balance
in the
set!
expression because
the <
name
> in a
set!
is not evaluated.
If we did substitute for it, we would get
(set! 25 (- 25 amount))
, which makes no sense.

10
The phenomenon of a
single computational object being accessed by more than one name is
known as
aliasing
. The joint bank account situation illustrates
a very simple example of an alias. In section 
3.3
we will see much more complex examples, such as “distinct” compound
data structures that share parts. Bugs can occur in our programs if
we forget that a change to an object may also, as a “side effect,”
change a “different” object because the two “different” objects
are actually a single object appearing under different aliases. These
so-called
side-effect bugs
are so difficult to locate and to
analyze that some people have proposed that programming languages be
designed in such a way as to not allow side effects or aliasing
(Lampson et al. 1981; Morris, Schmidt, and Wadler 1980).

11
In view of this, it is ironic that introductory programming
is most often taught in a highly imperative style. This may be a
vestige of a belief, common throughout the 1960s and 1970s, that
programs that call procedures must inherently be less efficient than
programs that perform assignments. (Steele (1977)
debunks this
argument.) Alternatively it may reflect a view that step-by-step
assignment is easier for beginners to visualize than procedure call.
Whatever the reason, it often saddles beginning programmers with
“should I set this variable before or after that one” concerns that can
complicate programming and obscure the important ideas.

BOOK: Structure and Interpretation of Computer Programs
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