Several of the Scheme procedures that we have written or studied in preceding labs presuppose that their arguments will meet specific preconditions – constraints on the types or values of its arguments. For example, the following procedure assumes that its input is a list of strings.
;;; Procedure:
;;; longest-string-length
;;; Parameters:
;;; strings, a list of strings
;;; Purpose:
;;; Find the length of the longest string in strings
;;; Produces:
;;; len, a non-negative integer
;;; Preconditions:
;;; [No additional]
;;; Postconditions:
;;; * len is the length of some string in `strings`. That is,
;;; There exists an i such that
;;; len = (string-length (list-ref strings i).
;;; * len is at least as large as the length of all strings in
;;; strings. That is,
;;; For all i, 0 <= i < (length strings),
;;; len >= (string-length (list-ref strings i))
(define longest-string-length
(lambda (strings)
(reduce max (map string-length strings))))
If some careless programmer invokes longest-string-length and
gives it, as an argument, the empty list, or a list in which one
of the elements is not a string, or perhaps even some Scheme value
that is not a list at all, the computation that the definition of
longest-string-length describes cannot be completed.
> (longest-string-length '())
Error! cdr: contract violation
Error! expected: pair?
Error! given: '()
> (longest-string-length '("hello" "a" 23))
Error! string-length: contract violation
Error! expected: string?
Error! given: 23
> (longest-string-length "strings")
Error! map: contract violation
Error! expected: list?
Error! given: "strings"
As you can see, none of these error messages are particularly helpful. Whose responsibility is it to handle these types of errors? As we will see, it is possible to share responsibility between the person who writes a procedure and the person who calls a procedure.
A procedure definition is like a contract between the author of the
definition and someone who invokes the procedure. The postconditions
of the procedure are what the author guarantees: When the computation
directed by the procedure is finished, the postconditions shall be
met. Usually the postconditions are constraints on the value of the
result returned by the procedure. For instance, the postcondition of the
sqr procedure,
(define sqr
(lambda (val)
(* val val)))
is that the result is the square of the argument val. (Alternately,
we might say that the square root of the result is val.)
The preconditions are the guarantees that the invoker of a procedure
makes to the author, the constraints that the arguments shall meet. For
instance, it is a precondition of the square procedure that val
is a number.
If the invoker of a procedure violates its preconditions, then the
contract is broken and the author’s guarantee of the postconditions
is void. (If val is, say, a list or a drawing, then the author can’t
very well guarantee to return its square. What would that even mean?) To
make it less likely that an invoker violates a precondition by mistake,
it is usual to document preconditions carefully and to include occasional
checks in one’s programs, ensuring that the preconditions are met before
starting a complicated computation.
Many of Scheme’s primitive procedures have such preconditions, which they enforce by aborting the computation and displaying a diagnostic message when the preconditions are not met:
> (/ 1 0)
Error! /: division by zero
> (log 0)
Error! /: log: undefined for 0
> (+ 1 'two)
Error! +: contract violation
Error! expected: number?
Error! given: 'two
Error! argument position: 2nd
Error! other arguments...:
> (length 116)
Error! . length: contract violation
Error! expected: list?
Error! given: 116
To enable us to enforce preconditions in the same way, most
implementations of Scheme provides a procedure named error, which
takes a string as its first argument. Calling the error procedure
aborts the entire computation of which the call is a part and causes
the string to be displayed as a diagnostic message.
For instance, we could enforce longest-string-length’s precondition
that its parameter be a non-empty list of strings by rewriting its
definition thus:
(define longest-string-length
(lambda (strings)
(when (or (not (list? strings))
(null? strings)
(not (all string? strings)))
(error "longest-string-length: expects a non-empty list of strings"))
(reduce max (map string-length strings))))
You may note that we’ve introduced the procedure all. (all pred? lst)
determines whether the predicate holds for every element of the list.
With these additions, longest-string-length enforces its preconditions.
> (longest-string-length '())
Error! longest-string-length: expects a non-empty list of strings
> (longest-string-length '("hello" "a" 23))
Error! longest-string-length: expects a non-empty list of strings
> (longest-string-length "strings")
Error! longest-string-length: expects a non-empty list of strings
Of course, while these error messages are better than the original error
messages, they don’t tell us the complete story. In particular, they
don’t tell us what the value of the incorrect parameter is. Fortunately,
error can take additional parameters, which it presents verbatim.
(define longest-string-length
(lambda (strings)
(when (or (not (list? strings))
(null? strings)
(not (all string? strings)))
(error "longest-string-length: expects a non-empty list of strings, given"
strings))
(reduce max (map string-length strings))))
With that addition, we get the following output.
> (longest-string-length '())
Error! longest-string-length: expects a non-empty list of strings, given ()
> (longest-string-length '("hello" "a" 23))
Error! longest-string-length: expects a non-empty list of strings, given ("hello" "a" 23)
> (longest-string-length "strings")
Error! longest-string-length: expects a non-empty list of strings, given "strings"
Isn’t that much nicer?
Including precondition testing in your procedures often makes them markedly easier to analyze and check, so we recommend the practice, especially during program development. There is a trade-off, however: It takes time to test the preconditions, and that time will be consumed on every invocation of the procedure. Since time is often a scarce resource, it makes sense to save time by skipping the test when you can prove that the precondition will be met. This often happens when you, as programmer, control the context in which the procedure is called as well as the body of the procedure itself.
For example, in the preceding definition of longest-string-length,
although it is useful to test the precondition when the procedure is
invoked “from outside” by a potentially irresponsible caller, if we
are only using it as a helper to another procedure that verifies that
it has a list of strings, it is a waste of time to repeat the
potentially expensive tests.
One solution to this problem is to replace the definition of
longest-string-length with two separate procedures, a “husk” and a
“kernel”. The husk interacts with the outside world, performs the
precondition test, and launches the procedure. The kernel is supposed
to be invoked only when the precondition can be proven true; its job
is to perform the main work of the original procedure, as efficiently
as possible:
(define longest-string-length
(lambda (strings)
(when (or (not (list? strings))
(null? strings)
(not (all string? strings)))
(error "longest-string-length: expects a non-empty list of strings, given"
strings))
(longest-string-length-kernel strings)))
(define longest-string-length-kernel
(lambda (strings)
(reduce max (map string-length strings))))
The kernel has the same preconditions as the husk procedure, but does not need to enforce them, because we invoke it only in situations where we already know that the preconditions are satisfied.
The one weakness in this idea is that some potentially irresponsible caller might still call the kernel procedure directly, bypassing the husk procedure that he’s supposed to invoke. In a subsequent reading and lab, we’ll see that there are a few ways to put the kernel back inside the husk without losing the efficiency gained by dividing the labor in this way.
While the benefits of this approach may not immediately be obvious, when you start to write procedures the step through the elements of a list, you will find it helpful to avoid revisiting all of the elements of the list at every step.
Are we done? Mostly. However, instead of giving the same error message
for every type of error, we might customize error messages for the
particular kind of error, giving a different error message in each
case. The longest-string-length procedure is perhaps not the best example,
because all three kinds of errors are essentially a failure to provide
a non-empty list of strings, but we’ll use it as a demonstration anyway.
(define longest-string-length
(lambda (strings)
(cond
[(not (list? strings))
(error "longest-string-length: expects a list of strings, received a non-list:"
strings)]
[(null? strings)
(error "longest-string-length: expects a *non-empty* list of strings, received an empty list")]
[(not (all string? strings))
(error "longest-string-length: expects a list of strings, received a list with non-strings:"
strings)]
[else
(longest-string-length-kernel strings)])))
(define longest-string-length-kernel
(lambda (strings)
(reduce max (map string-length strings))))
Now, our messages are a bit more informative.
> (longest-string-length '())
Error! longest-string-length: expects a *non-empty* list of strings, received an empty list
> (longest-string-length '("hello" "a" 23))
Error! longest-string-length: expects a list of strings, received a list with non-strings: ("hello" "a" 23)
> (longest-string-length "strings")
Error! longest-string-length: expects a list of strings, received a non-list: "strings"
Here’s a pattern we often use when we include precondition checking in our procedures.
(define safe-procedure
(lambda (parameters)
(cond
[(precondition-guard-1)
(error "failed first precondition" parameters)]
[(precondition-guard-2)
(error "failed second precondition" parameters)]
...
[(precondition-guard-n)
(error "failed last precondition" parameters)]
[else
(procedure-kernel parameters)])))
In an earlier class session, you may have written 6P documentation for a quadratic root finding procedure similar to the following:
;;; Procedure
;;; quadratic-root
;;; Parameters:
;;; a, a real number
;;; b, a real number
;;; c, a real number
;;; Purpose:
;;; Find the first root of the polynomial ax^2+bx+c
;;; Produces:
;;; root, a real number
;;; Preconditions:
;;; FORTHCOMING
;;; Postconditions:
;;; root = ( -b + sqrt(b^2-4*ac) ) / (2*a)
(define quadratic-root
(lambda (a b c)
(/ (+ (- b)
(sqrt (- (* b b) (* 4 a c))))
(* 2 a))))
a. What are the preconditions for quadratic-root? Document them in the 6Ps.
b. Following a pattern similar to safe-procedure, add a cond statement to quadratic-root and verify its preconditions.
The general framework for this reading is taken from a similar reading from spring 2018. The framework in that reading was taken from another similar reading from spring 2017. We have changed the examples used.
It is likely that the original version of this reading was written by John David Stone in the misty eons of time, or at least on 4 February 2000. Unfortunately, Mr. Stone’s early work on CSC 151 is no longer available online at its original address. What appears to be an archived version is available in the Internet Archive Wayback Machine. A modified version by Prof. Rebelsky from 15 September 2000 is still available. Between those times and the more recent readings, we’ve removed the dependency on recursion and added a variety of related text.