reference initialization

[Hongwei Xi <hwxi@ececs.uc.edu>]

> Wrong. You have references, which are quite better than pointers
> (they are typed, and necessarily initialized)

I have given some thoughts on this one.

I find that it is really not a good design choice to initialize
every reference in all ML-like languages (yes, I well realize
the difficulty in not doing so). Here is my argument.

The common wisdom is that if we initialize every reference
immediately after it is created then we shall never read from
an uninitialized reference. But this is a lame argument.

Suppose I use a reference 'x'. If I know what the initial value
of 'x' should be, I'll of course prefer to initialize it with that
value. Now suppose I don't, that is, I intend to assign a value
to 'v' later (maybe in a loop or in a conditional branch)

(1) ML strategy: initialize x with any value 'v' of an appropriate
type (sometimes, such a 'v' is difficult to find or takes time to
construct (consider 'x' to be a large array)).

Now suppose I make a mistake, forgetting to assign a value to 'x'
later. What happens is that the computation now happily use the
*wrong* initial value and sings the song "well-typed-program-can-
never-go-wrong" until a (most likely) wrong answer or exception is
returned.

(2) Java Strategy: let us say we have the same scenario as before
but 'x' is not initialized. If I read from x before assigning a value
to 'x', the execution abnormally stops (yes, we need run-time checking).
I think this is much better than to carry the execution further until a
wrong answer or exception is returned.

Can you still say that the ML strategy is better than the Java
strategy? I thus argue that it is better using dynamic checking
to detect reading from uninitialized reference than simply
assigning a value to every reference upon its creation.

To summarize, my bottom line question is: what is really achieved
by assigning a reference a (wrong) initial value? Isn't this just
like an ostrich solving its problem by burying its head in sand?

Of course, another problem with the ML strategy is efficiency loss
(which, though, is often negligible as discussed here before)

--Hongwei Xi

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