On this page:
22 The Poetry Of S-expressions
22.1 Trees
22.2 Forests
22.3 S-expressions
22.4 Designing With Intertwined Data
22.5 Simplifying Functions
23 Incremental Refinement
23.1 Data Analysis
23.2 Refining Data Definitions
23.3 Refining Functions
24 Refining Interpreters
24.1 Interpreting Expressions
24.2 Interpreting Variables
24.3 Interpreting Functions
24.4 Interpreting Everything
25 The Commerce Of XML
25.1 XML as S-expressions
25.2 Rendering XML Enumerations
25.3 Domain-Specific Languages
25.4 Reading XML
26 Simultaneous Processing
26.1 Processing Two Lists Simultaneously:   Case 1
26.2 Processing Two Lists Simultaneously:   Case 2
26.3 Processing Two Lists Simultaneously:   Case 3
26.4 Function Simplification
26.5 Designing Functions that Consume Two Complex Inputs
26.6 Exercises on Processing Two Complex Inputs
27 Summary

IV Intertwined Data

You might think that the data definitions for lists and natural numbers are quite unusual. These data definitions refer to themselves, and in all likelihood, they are the first such definitions you have ever encountered. As it turns out, many classes of data require even more complex data definitions than these two. Common generalizations involve many self-references in one data definition or a bunch of data definitions that refer to each other. These forms of data have become ubiquitous and it is therefore critical for a programmer to learn to cope with any collection of data definitions. And that’s what the design recipe is all about.

This part starts with a generalization of the design recipe so that it works for all forms of structural data definitions. Next, it introduces the concept of iterative refinement because complex data definitions are not developed in one fell swoop but in several stages. Indeed, the use of iterative refinement is one of the reasons why all programmers are little scientists and why our discipline uses the word “science” in its American name. Two last chapters illustrate these ideas: one explains how to design on interpreter for BSL and another one is about processing XML, one data exchange language for the Web. The last chapter expands the design recipe one more time, applying it to function that process two complex arguments at the same time.

22 The Poetry Of S-expressions

Programming resembles poetry in many ways. For example, programmers—like poets—practice their skill on seemingly pointless ideas. In this spirit, this chapter introduces increasingly complex forms of data without a true purpose. Even when we provide a motivational background, the chosen kinds of data are “purist,” and it is unlikely that you will ever encounter these particular kinds of data again.

Nevertheless, this chapter shows the full power of the design recipe and introduces you to the kinds of data that real-world programs cope with. To connect this material with what you will encounter in your life as a programmer, we label each section with appropriate names: trees, forests, XML. The last one is a bit misleading, because it is really about S-expressions; the connection between S-expressions and XML is clarified in The Commerce Of XML, which in contrast to this chapter, comes much closer to real-world uses of complex forms of data.

22.1 Trees

All of us have a family tree. One way to draw a family tree is to add a node every time a child is born. From the node, we can draw connections to the father and mother of the child. For those people in the tree whose parents are unknown, there is no connection to draw. The result is a so-called ancestor family tree because, given any person in the tree, we can find all of the person’s known ancestors.

Figure 59: A family tree

Figure 59 displays a three-tier family tree. Gustav is the child of Eva and Fred, while Eva is the child of Carl and Bettina. In addition to people’s names and family relationships, the tree also records years of birth and eye colors. Based on this sketch, you can easily imagine a family tree reaching back many generations and one that records other kinds of information.

Once a family tree is large, it makes sense to represent it as data and to design programs that process this kind of data. Given that a node in a family tree combines five pieces of information—the father, the mother, the name, the birth date, and the eye color—we should define a structure type:

(define-struct child [father mother name date eyes])

The structure type definition calls a data definition:
; A Child is a structure:
;   (make-child Child Child String N String)
While this data definition looks straightforward, it is also useless. It refers to itself but, because it doesn’t have any clauses, there is no way to create a proper instance Child. Roughly, we would have to write

(make-child (make-child (make-child ...) ...) ...)

without end. To avoid such pointless data definitions, we demand that a self-referential data definition has several clauses and that at least one of them does not refer back to the data definition.

Let’s postpone the data definition for a moment; let’s experiment instead. Suppose we are about to add a child to an existing family tree and that we already have representations for the parents. In that case, we can simply construct a new child structure. For example, to represent Adam in a program that already represents Carl and Bettina, it suffices to add the following child structure:

(define Adam (make-child Carl Bettina "Adam" 1950 "hazel"))

assuming Carl and Bettina stand for representations of Adam’s parents.

One problem is that we don’t always know a person’s parents, and its solution points to the correct data definitions. In the family tree of figure 59, we don’t know Bettina’s parents. Yet, even if we don’t know a person’s father or mother, we must still use some piece of data for the parent fields in a child representation of Bettina. Whatever data we choose, it must signal an absence of information. On the one hand, we could use #false, "none", or '() from the pool of existing values. On the other hand, we should really say that it is missing information about a family tree, and we can achieve this objective best with the introduction of a structure type with an appropriate name:

(define-struct no-parent [])

Now, to construct a child structure for Bettina, we say

(make-child (make-no-parent) (make-no-parent) "Bettina" 1926 "green")

Of course, if only one piece of information is missing, we fill just that field with this special value.

The experiment suggests two insights. First, we are not looking for a data definition that describes how to generate instances of child structures but for a data definition that describes how to represent family trees. Second, the data definition consists of two clauses, one for the nodes describing unknown family trees and another one for known family trees:
(define-struct no-parent [])
(define-struct child [father mother name date eyes])
; A FT (family tree) is one of:
;  (make-no-parent)
;  (make-child FT FT String N String)
Since the “no parent” tree is going to show up a lot in our programs, we define MTFT as a short-hand and revise the data definition a bit:
(define MTFT (make-no-parent))
; A FT (family tree) is one of:
;  (make-child FT FT String N String)
The use of a constant in the data definition should not bother you. You can replace a name by its value.

Following the design recipe from Designing With Self-Referential Data Definitions, we use the data definition to create examples of family trees. Specifically, we translate the family tree in figure 59 into our data representation. The information for Carl is easy to translate into data:

(make-child MTFT MTFT "Carl" 1926 "green")

Bettina and Fred are represented with similar instances of child. The case for Adam calls for nested children, one for Carl and one for Bettina:
(make-child (make-child MTFT MTFT "Carl" 1926 "green")
            (make-child MTFT MTFT "Bettina" 1926 "green")
Since the records for Carl and Bettina are also needed to construct the records for Dave and Eva, it is better to introduce definitions that name specific instances of child and to use the variable names elsewhere. Figure 60 illustrates this approach for the complete data representation of the family tree from figure 59. Take a close look; the tree serves as our running example for the following design exercise.

; Oldest Generation:
(define Carl (make-child MTFT MTFT "Carl" 1926 "green"))
(define Bettina (make-child MTFT MTFT "Bettina" 1926 "green"))
; Middle Generation:
(define Adam (make-child Carl Bettina "Adam" 1950 "hazel"))
(define Dave (make-child Carl Bettina "Dave" 1955 "black"))
(define Eva (make-child Carl Bettina "Eva" 1965 "blue"))
(define Fred (make-child MTFT MTFT "Fred" 1966 "pink"))
; Youngest Generation:
(define Gustav (make-child Fred Eva "Gustav" 1988 "brown"))

Figure 60: A data representation of the sample family tree

Instead of designing a concrete function on family trees, let us first look at the generic organization of such a function. That is, let us work through the design recipe as much as possible without having a concrete task in mind. We start with the header material, i.e., step 2 of the recipe:
; FT -> ???
; ...
(define (fun-for-FT a-ftree) ...)
Even though we aren’t stating the purpose of the function, we do know that it consumes a family tree and that this form of data is the main input. The “???” in the signature says that we don’t know what kind of data the function produces; the “...” remind us that we don’t know its purpose.

The lack of purpose implies that we cannot make up functional examples. Nevertheless, we can exploit the organization of the data definition for FT to design a template. Since it consists of two clauses, the template must consist of a cond expression with two clauses:
; FT -> ???
(define (fun-for-FT a-ftree)
    [(no-parent? a-ftree) ...]
    [else ; (child? a-ftree)
The first deals with MTFT, the second with child structures.

In case the argument to fun-for-FT satisfies no-parent?, the structure contains no additional data, so the first clause is complete. For the second clause, the input contains five pieces of data, which we indicate with five selectors in the template:
; FT -> ???
(define (fun-for-FT a-ftree)
    [(no-parent? a-ftree) ...]
     (... (child-father a-ftree) ...
      ... (child-mother a-ftree) ...
      ... (child-name a-ftree) ...
      ... (child-date a-ftree) ...
      ... (child-eyes a-ftree) ...)]))

The last addition to templates concerns self-references. If a data definition refers to itself, the function is likely to recur and templates indicate so with suggestive natural recursions. Unlike lists, the data definition for family trees requires two self-references and the template therefore needs two recursions:
; FT -> ???
(define (fun-for-FT a-ftree)
    [(no-parent? a-ftree) ...]
      (... (fun-for-FT (child-father a-ftree)) ...
       ... (fun-for-FT (child-mother a-ftree)) ...
       ... (child-name a-ftree) ...
       ... (child-date a-ftree) ...
       ... (child-eyes a-ftree) ...)]))
Specifically, fun-for-FT is applied to the data representation for fathers and mothers in the second cond clause, because the second clause of the data definition contains corresponding self-references.

Let us now turn to a concrete function, blue-eyed-child?. Its purpose is to determine whether any child structure in a given family tree has blue eyes. You may copy, paste, and rename fun-foor-FT to get its template; we replace “???” with Boolean and add a purpose statement;
; FT -> Boolean
; does a-ftree contain a child
; structure with "blue" in the eyes field
(define (blue-eyed-child? a-ftree)
    [(no-parent? a-ftree) ...]
      (... (child-name a-ftree) ...
       ... (child-date a-ftree) ...
       ... (child-eyes a-ftree) ...
       ... (blue-eyed-child? (child-father a-ftree)) ...
       ... (blue-eyed-child? (child-mother a-ftree)) ...)]))
When you work in this fashion, you need to replace the template’s generic name with a specific one.

Checking with our recipe, we realize that we need to backtrack and develop some examples before we move on to the definition step. If we start with Carl, the first person in the family tree, we see that Carl’s family tree does not contain a node with a "blue" eye color. Specifically, the node representing Carl says the eye color is "green" and, given that Carl’s ancestor trees are empty, they cannot possibly contain a node with "blue" eye color:

(check-expect (blue-eyed-child? Carl) #false)

In contrast, Gustav contains a node for Eva who does have blue eyes:

(check-expect (blue-eyed-child? Gustav) #true)

Now we are ready to define the actual function. The function distinguishes between two cases: a no-parent node and a child node. For the first case, the answer should be obvious even though we haven’t made up any examples. Since the given family tree does not contain any child node whatsoever, it cannot contain one with "blue" as eye color. Hence the result in the first cond clause is #false.

For the second cond clause, the design requires a lot more work. Again following the design recipe, we first remind ourselves what the expressions in the template accomplish:
  1. (child-name a-ftree) extracts the child’s name;

  2. (child-date a-ftree) extracts the child’s date of birth

  3. (child-eyes a-ftree), which extracts the child’s eye color

  4. according to the purpose statement for the function,

    (blue-eyed-child? (child-father a-ftree))

    determines whether some node in the father’s FT has "blue" eyes; and

  5. (blue-eyed-child? (child-mother a-ftree)) determines whether someone in the mother’s FT has blue eyes;

It is now time to combine the values of these expressions in an appropriate manner.

Clearly, if the given child structure contains "blue" in the eyes field, the function’s answer must be true. Furthermore, the expressions concerning names and birth dates are useless, which leaves us with the two recursive calls. As stated, (blue-eyed-child? (child-father a-ftree)) processes the father’s family tree and (blue-eyed-child? (child-mother a-ftree)) the mother’s. If either of these expressions returns #true, a-ftree contains a child with "blue" eyes—even if we don’t know where exactly this node is located. Since the consideration of the father’s tree, the mother’s tree, and the child node itself covers all nodes in a-ftree, there are no other possibilities.

Our analysis suggests that the result should be #true if one of the following three expressions is #true:
  • (string=? (child-eyes a-ftree) "blue")

  • (blue-eyed-child? (child-father a-ftree))

  • (blue-eyed-child? (child-mother a-ftree))

which, in turn, means we need to combine these expressions with or:
(or (string=? (child-eyes a-ftree) "blue")
    (blue-eyed-child? (child-father a-ftree))
    (blue-eyed-child? (child-mother a-ftree)))
Figure 61 pulls everything together in a single definition.

; FT -> Boolean
; does a-ftree contain a child
; structure with "blue" in the eyes field
(check-expect (blue-eyed-child? Carl) #false)
(check-expect (blue-eyed-child? Gustav) #true)
(define (blue-eyed-child? a-ftree)
    [(no-parent? a-ftree) #false]
    [else (or (string=? (child-eyes a-ftree) "blue")
              (blue-eyed-child? (child-father a-ftree))
              (blue-eyed-child? (child-mother a-ftree)))]))

Figure 61: Finding a blue-eyed child in an ancestor tree

Since this function is the very first one to use two recursions, we simulate the Stepper’s action for (blue-eyed-child? Carl) to give you an impression of how it all works:

   (blue-eyed-child? Carl)

== (blue-eyed-child? (make-child MTFT MTFT "Carl" 1926 "green"))



  [(no-parent? (make-child MTFT MTFT "Carl" 1926 "green")) #false]


    (or (string=?

          (child-eyes (make-child MTFT MTFT "Carl" 1926 "green"))



          (child-father (make-child MTFT MTFT "Carl" 1926 "green")))


          (child-mother (make-child MTFT MTFT "Carl" 1926 "green"))))])


(or (string=?

      (child-eyes (make-child MTFT MTFT "Carl" 1926 "green"))



      (child-father (make-child MTFT MTFT "Carl" 1926 "green")))


      (child-mother (make-child MTFT MTFT "Carl" 1926 "green"))))


(or (string=? "green" "blue")


      (child-father (make-child MTFT MTFT "Carl" 1926 "green")))


      (child-mother (make-child MTFT MTFT "Carl" 1926 "green"))))


(or #false

    (blue-eyed-child? MTFT)

    (blue-eyed-child? MTFT))

== (or #false #false #false)

== #false

The calculation uses several steps where we replace equals by equals, assuming appropriate auxiliary calculations, something the Stepper wouldn’t do. For example:

   (blue-eyed-child? MTFT)

== (blue-eyed-child? (make-no-parent))



  [(no-parent? (make-no-parent)) #false]


    (or (string=? (child-eyes (make-no-parent)) "blue")

        (blue-eyed-child? (child-father (make-no-parent)))

        (blue-eyed-child? (child-mother (make-no-parent))))])



  [#true #false]


    (or (string=? (child-eyes (make-no-parent)) "blue")

        (blue-eyed-child? (child-father (make-no-parent)))

        (blue-eyed-child? (child-mother (make-no-parent))))])

== #false

We trust you have seen such auxiliary calculations in math.

Exercise 253. Develop count-persons. The function consumes a family tree node and counts the child structures in the tree.

Exercise 254. Develop the function average-age. It consumes a family tree node and the current year. It produces the average age of all child structures in the family tree.

Exercise 255. Develop the function eye-colors, which consumes a family tree node and produces a list of all eye colors in the tree. An eye color may occur more than once in the resulting list.

Hint Use the append operation to concatenate lists:

     (append (list 'a 'b 'c) (list 'd 'e))

  == (list 'a 'b 'c 'd 'e)

We discuss the development of functions such as append in section Simultaneous Processing.

Exercise 256. Suppose we need the function blue-eyed-ancestor?. It is like blue-eyed-child? but responds with #true only when an ancestor, not the given child node, has blue eyes.

Even though the functions differ, their signatures are the same:
; FT -> Boolean
; does a-ftree contain a child node with blue eyes
(define (blue-eyed-ancestor? a-ftree) ...)
To appreciate the difference, we take a look at Eva:

(check-expect (blue-eyed-child? Eva) #true)

Eva is blue-eyed. Because she does not have a blue-eyed ancestor, however, we get

(check-expect (blue-eyed-ancestor? Eva) #false)

Now suppose a friend comes up with this solution:
(define (blue-eyed-ancestor? a-ftree)
    [(no-parent? a-ftree) #false]
    [else (or (blue-eyed-ancestor? (child-father a-ftree))
              (blue-eyed-ancestor? (child-mother a-ftree)))]))
Explain why this function fails the above test. What is the result of (blue-eyed-ancestor? A) no matter which A you choose? Can you fix your friend’s solution?

22.2 Forests

It is a short step from a family tree to an entire forest of information about families:
; A FF (family forest) is one of:
;  '()
;  (cons FT FF)
A family forest could represent the information a town maintains about all its families. Here are some trees excerpts from figure 59 arranged as forests:
(define ff1 (list Carl Bettina))
(define ff2 (list Fred Eva))
(define ff3 (list Fred Eva Carl))
The first two forests contain two unrelated families, and the third one illustrates that unlike in real forests, trees in family forests can overlap.

Here is a representative problem concerning family trees:

Sample Problem: Design the function blue-eyed-child-in-forest?, which determines whether a family forest contains a child with "blue" in the eyes field.

The straightforward solution is displayed in figure 62.

; FF -> Boolean
; does the forest contain any child node with "blue" eyes
(check-expect (blue-eyed-child-in-forest? ff1) #false)
(check-expect (blue-eyed-child-in-forest? ff2) #true)
(check-expect (blue-eyed-child-in-forest? ff3) #true)
(define (blue-eyed-child-in-forest? a-forest)
    [(empty? a-forest) #false]
    [else (or (blue-eyed-child? (first a-forest))
              (blue-eyed-child-in-forest? (rest a-forest)))]))

Figure 62: Finding a blue eyed child in a family forest

We leave it to you to figure out the signature, the purpose statement, and the examples for this solution. As for the template, the design can employ the list template because blue-eyed-child-in-forest? consumes lists. If each item on the list were a structure with an eyes field and nothing else, the function would iterate over those structures using the selector function for the eyes field and a string comparison. In this case, each item is a family tree but luckily, we already know how to process family trees.

Let us step back for a moment and inspect how we explained figure 62. The starting point is a pair of data definitions where the second refers to the first and both refer to themselves. The result is a pair of functions where the second refers to the first and both refer to themselves. In other words, the function definitions refer to each other the same way the data definitions refer to each other. To some extent, this relationship is a simple generalization of what we have seen so far. The difference is that two data definitions and two functions are involved, but not counting the self-references of both data definitions, we have encountered this situation before in some of our projects; we just happen to gloss over it. Instead of dwelling on the idea in the context of trees, we move on to S-expressions and articulate a generalized design recipe afterwards.

Exercise 257. Reformulate the data definition for FF with the List-of abstraction. Now do so for the signature of blue-eyed-child-in-forest?. Finally, define blue-eyed-child-in-forest? using one of the list abstractions from the preceding chapter.

Exercise 258. Design the function average-age. It consumes a family forest and a year, specified as a natural number. From this data, it produces the average age of all child nodes in the forest. Note If the trees in this forest overlap, the result isn’t a true average because some people may contribute more than others. For this exercise, act as if the trees don’t overlap.

22.3 S-expressions

While Intermezzo: Quote, Unquote introduced S-expressions on an informal basis, it is possible to describe them with a combination of three data definitions:
; An S-expr (S-expression) is one of:
;  Atom
;  SL
; An SL (S-list) is one of:
;  '()
;  (cons S-expr SL)
; An Atom is one of:
;  Number
;  String
;  Symbol
Recall that Symbols look like strings with a single quote at the beginning and with no quote at the end.

The idea of S-expressions is due to John McCarthy and his Lispers, who created S-expressions in 1958 so that they could process Lisp programs with other Lisp programs. This seemingly circular reasoning may sound esoteric but as mentioned in Intermezzo: Quote, Unquote, S-expressions are a versatile form of data that is often rediscovered, most recently with applications to the world wide web. Working with S-expressions thus prepares a discussion of how to design functions for highly intertwined data definitions.

Exercise 259. Define the atom? function.

Up to this point in this book, no data has required a data definition as complex as the one for S-expressions. And yet, with one extra hint, you can design functions that process S-expressions if you follow the design recipe. To demonstrate this point, let us work through a specific example:

Sample Problem: Design the function count, which determines how many times some symbol sy occurs in some S-expression sexp.

While the first step calls for data definitions and appears to have been completed, remember that it also calls for the creation of data examples, especially when the definition is complex.

A data definition is supposed to be a prescription of how to create data, and its “test” is whether it is usable. One point that the data definition for S-expr makes is that every Atom is an element of S-expr, and you know that Atoms are easy to fabricate:
In the same vein, every SL is an S-expr, and you know how to make up lists:
(cons 'hello (cons 20.12 (cons "world" '())))
(cons (cons 'hello (cons 20.12 (cons "world" '())))
The first two are obvious, the third one deserves a second look. It repeats the second S-expr but nested inside (cons ... '()). What this means is that it is a list that contains a single item, namely, the second example. You can simplify the example with list:
(list (cons 'hello (cons 20.12 (cons "world" '()))))
; or
(list (list 'hello 20.12 "world"))

Indeed, with the quotation mechanism of Intermezzo: Quote, Unquote it is even easier to write down S-expressions. Here are the last three repeated in this notation:
> '()


> '(hello 20.12 "world")

(list 'hello #i20.12 "world")

> '((hello 20.12 "world"))

(list (list 'hello #i20.12 "world"))

To help you out, we evaluate these examples in the interactions area of DrRacket so that you can see the result, which is closer to the above constructions than the quote notation.

With quote, it is quite easy to make up complex examples:
> '(define (f x)
     (+ (* 3 x x) (* -2 x) 55))



 (list 'f 'x)

 (list '+ (list '* 3 'x 'x) (list '* -2 'x) 55))

This example may strike you as odd because it looks like a definition in BSL but, as the interaction with DrRacket shows, it is just a piece of data. Here is another one:
> '((6 f)
    (5 e)
    (4 d))

(list (list 6 'f) (list 5 'e) (list 4 'd))

This piece of data looks like a table, associating letters with numbers. The last example is a piece of art:
> '(wing (wing (wing body wing) wing) wing)

(list 'wing (list 'wing (list 'wing 'body 'wing) 'wing) 'wing)

It is now time to write down the rather obvious header for count:
; S-expr Symbol -> N
; counts all occurrences of sy in sexp
(define (count sexp sy)
Since the header is obvious, we move on to functional examples. If the given S-expr is 'world and the to-be-counted symbol is 'world, the answer is obviously 1. Here are some more examples, immediately formulated as tests:
(check-expect (count 'world 'hello) 0)
(check-expect (count '(world hello) 'hello) 1)
(check-expect (count '(((world) hello) hello) 'hello) 2)
You can see how convenient quotation notation is for test cases. When it comes to templates, however, thinking in terms of quote or even list would be disastrous.

Before we move on to the template step, we need to give you one hint:

For intertwined data definitions, create one template per data definition. Create them in parallel. Make sure they refer to each other in the same way the data definitions do.

This hint sounds more complicated than it is. For our problem, it means we need three templates:
  1. one for count, which counts occurrences of symbols in S-exprs;

  2. one for a function that counts occurrences of symbols in SLs;

  3. and one for a function that counts occurrences of symbols in Atoms;

And here are three partial templates, with conditionals as suggested by the three data definitions:
(define (count sexp sy)
   [(atom? sexp) ...]
   [else ...]))
(define (count-sl sl sy)
    [(empty? sl) ...]
    [else ...]))
(define (count-atom at sy)
    [(number? at) ...]
    [(string? at) ...]
    [(symbol? at) ...]))
The template for count contains a two-pronged conditional, because the data definition for S-expr has two clauses. It uses the atom? function to distinguish the case for Atoms from the case for SLs. The template named count-sl consumes an element of SL and a symbol, and because SL is basically a list, count-sl also contains a two-pronged cond. Finally, count-atom is supposed to work for Atoms and Symbols. And this means that its template checks for the three distinct forms of data mentioned in the data definition of Atom.

The next step in the template creation process is to take apart compound pieces of data:
(define (count sexp sy)
   [(atom? sexp) ...]
   [else ...]))
(define (count-sl sl sy)
    [(empty? sl) ...]
    [else (... (first sl) ... (rest sl) ...)]))
(define (count-atom at sy)
    [(number? at) ...]
    [(string? at) ...]
    [(symbol? at) ...]))
Since only count-sl deals with constructed data, we add just two selector expressions.

The last step in the template creation process calls for an inspection of self-references in the data definitions. In our context, this means self-references and references from one data definition to another and (possibly) back. Let us inspect the cond lines in the three templates:
  1. The atom? line in count corresponds to the first line in the definition of S-expr, which points to Atom. To indicate this reference from one data definition to the other inside the template, we add (count-atom sexp sy), meaning we interpret sexp as an Atom and let the appropriate function deal with it.

  2. Following the same line of thought, the second cond line in count calls for the addition of (count-sl sexp sy).

  3. The empty? line in count-sl corresponds to a line in the data definition that makes no reference to another data definition.

  4. In contrast, the else line contains two selector expressions, and each of them extracts a different kind of value. Specifically, (first sl) is an element of S-expr, which means that we should wrap it in (count ...). After all, count is responsible for counting inside of arbitrary S-exprs. In the same vein, (rest sl) corresponds to a self-reference, and we know that we need to deal with those via recursive function calls.

  5. Finally, all three cases in Atom refer to atomic forms of data. Therefore the count-atom function does not need to change.

(define (count sexp sy)
   [(atom? sexp) (count-atom sexp sy)]
   [else (count-sl sexp sy)]))
(define (count-sl sl sy)
    [(empty? sl) ...]
    [else (... (count (first sl) sy)
           ... (count-sl (rest sl) sy) ...)]))
(define (count-atom at sy)
    [(number? at) ...]
    [(string? at) ...]
    [(symbol? at) ...]))

Figure 63: A template for S-expressions

Figure 63 presents the three complete templates. Filling in the blanks in these templates is straightforward:
; S-expr Symbol -> N
; counts all occurrences of sy in sexp
(define (count sexp sy)
   [(atom? sexp) (count-atom sexp sy)]
   [else (count-sl sexp sy)]))
; SL Symbol -> N
; counts all occurrences of sy in sl
(define (count-sl sl sy)
    [(empty? sl) 0]
    [else (+ (count (first sl) sy) (count-sl (rest sl) sy))]))
; Atom Symbol -> N
; counts all occurrences of sy in at
(define (count-atom at sy)
    [(number? at) 0]
    [(string? at) 0]
    [(symbol? at) (if (symbol=? at sy) 1 0)]))
With this idea in mind, you can easily explain any random line in these definitions:
  • [(atom? sexp) (count-atom sexp sy)] checks whether sexp is an atom. Hence, it interprets the S-expr as an Atom and calls count-atom on it. Doing so means the latter function counts how often sy occurs in sexpwhich is precisely what we want, but specialized to the appropriate kind of data.

  • [else (+ (count (first sl) sy) (count-sl (rest sl) sy))] means the given list consists of two parts: an S-expr and an SL. By using count and count-sl, the corresponding functions are used to count how often sy appears in each part, and the two numbers are added up—yielding the total number of sys in all of sexp.

  • [(symbol? at) (if (symbol=? at sy) 1 0)] tells us that if an Atom is a Symbol, sy occurs once if it is equal to sexp and otherwise it does not occur at all. Since the two pieces of data are atomic, there is no other possibility.

Voilà, you have systematically designed a function for S-expressions.

Exercise 260. In a sense, we designed one program that consists of three connected functions. To express this fact, we can use local to organize the definitions:
; S-expr Symbol -> N
; counts all occurrences of sy in sexp
(define (count sexp sy)
  (local (; S-expr Symbol -> N
          ; the main function
          (define (count-sexp sexp sy)
              [(atom? sexp) (count-atom sexp sy)]
              [else (count-sl sexp sy)]))
          ; SL Symbol -> N
          ; counts all occurrences of sy in sl
          (define (count-sl sl sy)
              [(empty? sl) 0]
              [else (+ (count-sexp (first sl) sy)
                       (count-sl (rest sl) sy))]))
          ; Atom Symbol -> N
          ; counts all occurrences of sy in at
          (define (count-atom at sy)
              [(number? at) 0]
              [(string? at) 0]
              [(symbol? at) (if (symbol=? at sy) 1 0)])))
    ;  IN
    (count-sexp sexp sy)))
Notice that we also renamed the first function to indicate that its primary argument is an S-expr.

Copy the above definition into DrRacket and validate with the test suite for count that it works properly.

The second argument to the local functions, sy, never changes. It is always the same as the original symbol. Hence you can eliminate it from the local function definitions and function applications. Do so and re-test the revised definition. In Simplifying Functions, we show you how to simplify these kinds of definitions even more—which is easy to do when you have designed functions systematically.

Exercise 261. Design depth. The function consumes an S-expression and determines its depth. An atom has a depth of 1. The depth of a list of S-expressions is the maximum depth of its items plus 1.

Exercise 262. Design the substitute function. It consumes an S-expression s and two symbols, old and new. The result is like s with all occurrences of old replaced by new.

Exercise 263. Reformulate the data definition for S-expr so that the first clause is expanded into the three clauses of Atom and the second clause uses the List-of abstraction.

Re-design the count function for this data definition. Note This kind of simplification is not always possible though experienced programmers tend to recognize and use such opportunities.

Exercise 264. Abstract the data definitions for S-expr and SL so that they abstract over the kinds of atoms that may appear.

22.4 Designing With Intertwined Data

The jump from self-referential data definitions to collections of data definitions with mutual references is far smaller than the one from data definitions for finite data to self-referential data definitions. Indeed, the design recipe for self-referential data definitions—see Designing With Self-Referential Data Definitionsneeds only minor adjustments to apply to this seemingly complex situation:
  1. The need for “nests” of mutually related data definitions is similar to the one for the need for self-referential data definitions. The problem statement deals with many distinct kinds of information, and one form of information refers to other kinds.

    Before you proceed in such situations, you should draw arrows on top of the data definitions that connect references to definitions. For example, the definition for S-expr contains references to SL and Atom, which you should connect with the two respective definitions. Similarly, the definition of SL contains one self-reference and one reference back to SL.

    Figure 64: Arrows for nests of data definitions

    Like self-referential data definitions, these nests also call for validation. At a minimum, you must be able to construct some examples for every individual data definition. Before you even start, make sure some of the data definitions contain clauses that do not refer to any of the other data definitions that come with this nest.

    You can, and should, also check the validity of self-referential data definitions with the creation of examples. Keep in mind that the definition may be invalid if it is impossible to generate examples from them.

  2. The key change is that you must design as many functions in parallel as there are data definitions. Each function specializes for one of the data definitions; all remaining arguments remain the same. Based on that, you start with a signature, a purpose statement, and a dummy definition for each function.

  3. Be sure to work through functional examples that use all self and mutual references in the nest of data definitions. For finding mistakes, it is best to derive the examples for the auxiliary functions from the examples for the primary function.

    Take a look at the examples for count above. It comes with three examples. The first one can obviously serve as an example for count-atom:

    (check-expect (count-atom 'world 'hello) 0)

    In contrast, the other two are good examples for count-sl:
    (check-expect (count '(world hello) 'hello) 1)
    (check-expect (count '(((world) hello) hello) 'hello) 2)

  4. For each function design the template according to its primary data definition. Use figure 37 to guide the template creation up to the last step.

    The last template creation step now calls for a check for all self and cross references. Use the data definitions annotated with arrows to guide this step. For each arrow, include a self-call or a cross-call from one function to another. See figure 65 for the arrow-annotated version of the templates for counting atoms in an S-expr.

    Figure 65: Arrows for mutual references in templates

    Last but not least, replace the arrows with actual function calls.

    Note Once again, you should notice the symmetry between the arrow-enriched data definition in figure 64 and the arrow-enriched template in figure 65. This symmetry is evidence that the design recipe provides a natural way for going from problems to solutions.

  5. For the design of the body we start with those cond lines that do not contain natural recursions or calls to other functions. They are called base cases. The corresponding answers are typically easy to formulate or are already given by the examples. After that, you deal with the self-referential cases and the cases of cross-function calls. Let the questions and answers of figure 39 guide you.

  6. Run the test suite. If an auxiliary function is broken, you will get two error reports if you followed our advice concerning examples. A single fix should eliminate both. Do make sure that running the tests covers all the pieces of the function.

22.5 Simplifying Functions

Exercise 260 shows how to use local to organize a function that deals with an intertwined form of data. This organization also helps simplify functions once we know that the data definition is final. To demonstrate this point, we explain how to simplify the solution of exercise 262.

Here is a complete definition of the substitute function, using local and three auxiliary functions as suggested by the data definition:
; S-expr Symbol Atom -> S-expr
; replaces all occurrences of old in sexp with new
(check-expect (substitute 'world 'hello 0) 'world)
(check-expect (substitute '(world hello) 'hello 'bye) '(world bye))
(check-expect (substitute '(((world) bye) bye) 'bye '42) '(((world) 42) 42))
(define (substitute sexp old new)
  (local (; S-expr -> S-expr
          (define (subst-sexp sexp)
              [(atom? sexp) (subst-atom sexp)]
              [else (subst-sl sexp)]))
          ; SL -> S-expr
          (define (subst-sl sl)
              [(empty? sl) '()]
              [else (cons (subst-sexp (first sl)) (subst-sl (rest sl)))]))
          ; Atom -> S-expr
          (define (subst-atom at)
              [(number? at) at]
              [(string? at) at]
              [(symbol? at) (if (symbol=? at old) new at)])))
    ;  IN
    (subst-sexp sexp)))
We have included test cases so that you can re-test the function after each edit.

Exercise 265. Copy and paste the above definition into DrRacket, including the test suite. Run and validate that the test suite passes. As you read along the remainder of this section, perform the edits and re-run the test suites to confirm the validity of our arguments.

Since we know that SL describes lists of S-expr, we can use map to simplify subst-sl:
(define (substitute.v1 sexp old new)
  (local (; S-expr -> S-expr
          (define (subst-sexp sexp)
              [(atom? sexp) (subst-atom sexp)]
              [else (subst-sl sexp)]))
          ; SL -> S-expr
          (define (subst-sl sl)
            (map subst-sexp sl))
          ; Atom -> S-expr
          (define (subst-atom at)
              [(number? at) at]
              [(string? at) at]
              [(symbol? at) (if (symbol=? at old) new at)])))
    ;  IN
    (subst-sexp sexp)))
Its original definition says that subst-sexp is applied to every item on sl; the new definition expresses the same idea more succinctly with map.

For the second simplification step, we need to introduce eq?, another built-in function of our programming language. All you need to know for now is that eq? determines whether two pieces of atomic data are equal. In other words, if s is a symbol and t is a number, (eq? s t) evaluates to #false because a string and a number can never be equal; but (eq? s t) produces #true, if t is also a symbol and spells exactly like s.

With this in mind, the third local function becomes a one-liner:
(define (substitute.v2 sexp old new)
  (local (; S-expr -> S-expr
          (define (subst-sexp sexp)
              [(atom? sexp) (subst-atom sexp)]
              [else (subst-sl sexp)]))
          ; SL -> S-expr
          (define (subst-sl sl)
            (map subst-sexp sl))
          ; Atom -> S-expr
          (define (subst-atom at)
            (if (eq? at old) new at)))
    ;  IN
    (subst-sexp sexp)))

At this point the last two local definitions consist of a single line. Furthermore, neither definition is recursive. Hence we can in-line the functions in subst-sexp, the first local function. In-lining means replacing (subst-atom sexp) with (if (eq? sexp old) new sexp), that is, we replace the parameter at with the actual argument sexp.While sexp is also a parameter, this substitution is really acceptable because it, too, stands in for an actual value. Similarly, for (subst-sl sexp) we put in (map subst-sexp sexp):
(define (substitute.v3 sexp old new)
  (local (; S-expr -> S-expr
          (define (subst-sexp sexp)
              [(atom? sexp) (if (eq? sexp old) new sexp)]
              [else (map subst-sexp sexp)])))
    ;  IN
    (subst-sexp sexp)))
All we are left with now is a function whose definition introduces one local function, which is called on the same major argument. If we systematically supplied the other two arguments, we would immediately see that the local function can be used in lieu of the outer one.

Here is the result of translating this last thought into code:
(define (substitute sexp old new)
    [(atom? sexp) (if (eq? sexp old) new sexp)]
    [else (map (lambda (s) (substitute s old new)) sexp)]))
Stop! Explain why we had to use lambda for this last simplification.

23 Incremental Refinement

When you develop real-world programs, you may confront complex forms of information and the problem of representing them with data. The best strategy to approach this task is to use iterative refinement, a well-known scientific process. A scientist’s problem is to represent a part of the real world using some form of mathematics. The result of the effort is called a model. The scientist then tests the model in many ways, in particular by predicting the outcome of experiments. If the discrepancies between the predictions and the measurements are too large, the model is refined with the goal of improving the predictions. This iterative process continues until the predictions are sufficiently accurate.

Consider a physicist who wishes to predict a rocket’s flight path. While a “rocket as a point” representation is simple, it is also quite inaccurate, failing to account for air friction. In response, the physicist may add the rocket’s rough contour and introduce the necessary mathematics to represent friction. This second model is a refinement of the first model. In general, a scientist iterates this process until the model predicts the rocket’s flight path with sufficient accuracy.

A programmer trained in a computer science department should proceed like this physicist. The key is to find an accurate data representation of the real-world information and functions that process them appropriately. Complicated situations call for a refinement process to get to a sufficient data representation combined with the proper functions. The process starts with the essential pieces of information and adds others as needed. Sometimes a programmer must refine a model after the program has been deployed because users request additional functionality.

So far we have used iterative refinement for you when it came to complex forms of data. This chapter illustrates iterative refinement as a principle of program development with an extended example, representing and processing (portions of) a computer’s file system. We start with a brief discussion of the file system and then iteratively develop three data representations. Along the way, we propose some programming exercises so that you see how the design recipe also helps modify existing programs.

23.1 Data Analysis

Before you turn off DrRacket, you want to make sure that all your work—your programs, your data—are stashed away somewhere. Otherwise you have to reenter everything when you fire up DrRacket next. So you ask your computer to save programs and data in files. A file is a sequence of small pieces of characters, also called bytes. For our purposes here, a file resembles a string.

Figure 66: A sample directory tree

On most computer systems, files are organized in directories or folders. Roughly speaking, a directory contains some files and some more directories. The latter are called subdirectories and may contain yet more subdirectories and files, and so on. The entire collection is also called a directory tree.

Figure 66 contains a graphical sketch of a small directory tree, and the picture explains why computer scientists call them trees. Contrary to computer scientists, the figure shows the tree growing upwards, with a root directory named TS. The root directory contains one file, called read!, and two subdirectories, called Text and Libs, respectively. The first subdirectory, Text, contains only three files; the latter, Libs, contains only two subdirectories, each of which contains at least one file. Finally each box has one of two annotations: a directory is annotated with DIR, and a file is annotated with a number, its size.

Exercise 266. How many times does a file name read! occur in the directory tree TS? Can you describe the path from the root directory to the two occurrences? What is the total size of all the files in the tree? What is the total size of the directory if each directory node has size 1? How many levels of directories does it contain?

23.2 Refining Data Definitions

Exercise 266 lists some of the questions that users routinely ask about directories. To answer such questions, the computer’s operating system provides programs that can answer just such questions. If you want to design such programs, you need to develop a data representation for directory trees.

In this section, we use iterative refinement to develop three such data representations. For each stage, we need to decide which attributes to include and which to ignore. Consider the directory tree in figure 66 and imagine how it is created. When a user first creates a directory, it is empty. As time goes by, the user adds files and directories. In general, a user refers to files by names but mostly thinks of directories as containers of other things.

Model 1 Our thought experiment suggests that our first model should focus on files as atomic entities with a name and directories as containers. Here is a data definition that deals with directories as lists and files as symbols, i.e., their names:
; A Dir.v1 (short for directory) is one of:
;  '()
;  (cons File.v1 Dir.v1)
;  (cons Dir.v1 Dir.v1)
; A File.v1 is a Symbol.
The names have a .v1 suffix to distinguish them from future refinements.

Exercise 267. Translate the directory tree in figure 66 into a data representation according to model 1.

Exercise 268. Design the function how-many, which determines how many files a given Dir.v1 contains. Remember to follow the design recipe; exercise 267 provides you with data examples.

Model 2 If you solved exercise 268, you know that this first data definition is still reasonably simple. But, it also obscures the nature of directories. With this first representation, we would not be able to list all the names of the subdirectories of some given directory. To model directories in a more faithful manner than containers, we must introduce a structure type that combines a name with a container:

(define-struct dir [name content])

This new structure type, in turn, suggests the following revision of the data definition:
; A Dir.v2 is a structure:
;   (make-dir Symbol LOFD)
; A LOFD (short for list of files and directories) is one of:
;  '()
;  (cons File.v2 LOFD)
;  (cons Dir.v2 LOFD)
; A File.v2 is a Symbol.
Note how the data definition for Dir.v2 refers to the definition for LOFDs and the one for LOFDs refers back to that of Dir.v2. The two definitions are mutually recursive.

Exercise 269. Translate the directory tree in figure 66 into a data representation according to model 2.

Exercise 270. Design the function how-many, which determines how many files a given Dir.v2 contains. Exercise 269 provides you with data examples. Compare your result with that of exercise 268.

Exercise 271. Show how to equip a directory with two more attributes: size and readability. The former measures how much space the directory itself (as opposed to its files and subdirectories) consumes; the latter specifies whether anyone else besides the user may browse the content of the directory.

Model 3 Like directories, files have attributes. To introduce these, we proceed just as above. First, we define a structure for files:

(define-struct file [name size content])

Second, we provide a data definition:
; A File.v3 is a structure:
;   (make-file Symbol N String)
As indicated by the field names, the symbol represents the name of the file, the natural number its size, and the string its content.

Finally, let us split the content field of directories into two pieces: a list of files and a list of subdirectories. This change requires a revision of the structure type definition:

(define-struct dir.v3 [name dirs files])

Here is the refined data definition:
; A Dir.v3 is a structure:
;   (make-dir.v3 Symbol Dir* File*)
; A Dir* is one of:
;  '()
;  (cons Dir.v3 Dir*)
; A File* is one of:
;  '()
;  (cons File.v3 File*)
Following a convention in computer science, the use of * as the ending of a name suggests “many” and is a marker distinguishing the name from similar ones: File.v3 and Dir.v3.

Exercise 272. Translate the directory tree in figure 66 into a data representation according to model 3. Use "" for the content of files.

Exercise 273. Design the function how-many, which determines how many files a given Dir.v3 contains. Exercise 272 provides you with data examples. Compare your result with that of exercise 270.

Given the complexity of the data definition, contemplate how anyone can design correct functions. Why are you confident that how-many produces correct results?

Exercise 274. Use List-of to simplify the data definition Dir.v3. Then use ISL+’s list processing functions from figure 55 to simplify the function definition(s) for the solution of exercise 273.

Starting with a simple representation of the first model and refining it step by step, we have developed a reasonably accurate data representation for directory trees. Indeed, this third data representation captures the nature of a directory tree much more faithfully than the first two. Based on this model, we can create a number of other functions that users expect from a computer’s operating system.

23.3 Refining Functions

To make the following exercises somewhat realistic, DrRacket comes with the "dir.ss" library from the first edition of this book. This teachpack introduces the two structure type definitions from model 3, though without the .v3 suffix. Furthermore, the teachpack provides a function that creates model 3-style data representations of directory trees on your computer:
; String -> Dir.v3
; creates a data representation of the directory that a-path identifies
(define (create-dir a-path) ...)
For example, if you open DrRacket and enter the following three lines into the definitions area:
(require htdp/dir)
(define d0 (create-dir "/Users/...")) ; on OS X
(define d1 (create-dir "/var/log/")) ; on OS X
(define d2 (create-dir "C:\\Users\\...")) ; on Windows
you get data representations of directories on your computer after you save and then run the program.The function informs you via print outs about inaccessible directories. Indeed, you could use create-dir to map the entire file system on your computer to an instance of Dir.v3. Warnings: (1) For large directory trees, DrRacket may need a lot of time to build a representation. Use create-dir on small directory trees first. (2) Do not define your own dir structure type. The teachpack already defines them, and you must not define a structure type twice.

Although create-dir delivers only a representation of a directory tree, it is sufficiently realistic to give you a sense of what it is like to design programs at that level. The following exercises illustrate this point. They use Dir to refer to the generic idea of a data representation for directory trees. Use the simplest data definition of Dir that allow you to complete the respective exercise. Feel free to use the data definition from exercise 274 and the functions from figure 55.

Exercise 275. Use create-dir to create data representations of some sample directories on your computer. Then use how-many from exercise 273 to count how many files they contain. Why are you confident that how-many produces correct results for these directories?

Exercise 276. Design find?. The function consumes a Dir and a file name and determines whether or not a file with this name occurs in the directory tree.

Exercise 277. Design the function ls, which lists the names of all files and directories in a given Dir.

Exercise 278. Design du, a function that consumes a Dir and computes the total size of all the files in the entire directory tree. Assume that storing a directory in a Dir structure costs 1 file storage unit. In the real world, a directory is basically a special file and its size depends on how large its associated directory is.

The remaining exercises rely on the notion of a path, which for our purposes, is a list of names:
; Path = [List-of Symbol]
; interpretation directions on how to find a file in a directory tree
For example, the path from TS to part1 in figure 66 is (list 'TS 'Text 'part1). Similarly, the path from TS to Code is (list 'TS 'Libs 'Code).

Exercise 279. Design find. The function consumes a directory d and a file name f. If (find? d f) is true, find produces a path to a file with name f; otherwise it produces #false.

Hint While it is tempting to first check whether the file name occurs in the directory tree, you have to do so for every single subdirectory. Hence it is better to combine the functionality of find? and find.

Challenge: The find function discovers only one of the two files named read! file in figure 66. Design find-all, which is generalizes find and produces the list of all paths that lead to f in d. What should find-all produce when (find? f d) is #false? Is this part of the problem really a challenge compared to the basic problem?

Exercise 280. Design the function ls-R, which lists the paths to all files in a given Dir. Challenge: Modify ls-R so that its result includes all paths to directories, too.

Exercise 281. Re-design find-all from exercise 279 using ls from exercise 280. This is design by composition, and if you solved the challenge part of exercise 280 your new function can find directories, too.

24 Refining Interpreters

DrRacket is a program. As you can imagine, it is a complex program, dealing with many different kinds of data. Like most complex programs, DrRacket also consists of many functions: one that allows programmers to edit text; another one that acts like the interactions area; a third one checks whether definitions and expressions are grammatical; and so on.

In this chapter, we show you how to design the function that implements the heart of the interactions area. Naturally, we use iterative refinement to design this evaluator. As a matter of fact, the very idea of focusing on the evaluator part of DrRacket is another instance of refinement, namely, the obvious one of implementing only one piece of functionality for a complex program.

Simply put, the interactions area performs the task of determining the values of expressions that you enter. After you click RUN, the interactions area knows about all the definitions. It is then ready to accept an expression that may refer to these definitions, to determine the value of this expression, and to repeat this cycle as often as you wish. For this reason, many people also refer to the interactions are as the read-eval-print loop, where eval is short for evaluator, a function is also called an interpreter.

Like this book, our refinement process starts with numeric BSL expressions. They are simple; they do not assume an understanding of definitions; and even your brother in fourth grade can determine their value. Once you understand this first step, you know the difference between a BSL expression and its representation. Next we move on to expressions with variables. The last step is to add definitions.

24.1 Interpreting Expressions

Our first task is to agree on a data representation for BSL programs, that is, we must figure out how to represent a BSL expression as a piece of BSL data. This sounds strange and unusual, but it is not difficult. Suppose we just want to represent numbers, additions, and multiplications for a start. Clearly, numbers can stand for numbers. Additions and multiplications, however, call for a class of compound data because they consist of an operator and two pieces.

Following the design recipes of this book, a straightforward way to represent additions and multiplications is to define two structure types: one for additions and another one for multiplications, each with two fields:
The intention is that the left field contains one operand—the one to the “left” of the operator—and the right field contains the other operand. The following table shows three examples:

BSL expression

representation of BSL expression



(+ 1 1)

(make-add 1 1)

(* 300001 100000)

(make-mul 300001 100000)

The next question is how we should represent an expression with subexpressions:

(+ (* 3 3) (* 4 4))

The surprisingly simple answer is that fields may contain any value. In this particular case, left and right may contain representations of expressions; and you may nest this as deep as you wish as the next table shows:

BSL expression

representation of BSL expression

(+ (* 1 1) 10)

(make-add (make-mul 1 1) 10)

(+ (* 3 3)
   (* 4 4))
(make-add (make-mul 3 3)
          (make-mul 4 4))
(+ (* (+ 1 2) 3)
   (* (* (+ 1 1)
(make-add (make-mul (make-add 1 2) 3)
          (make-mul (make-mul
                       (make-add 1 1)
As you can see, these examples cover all cases: numbers, variables, simple expressions, and nested expressions.

Exercise 282. Formulate a data definition for the representation of BSL expressions based on the structure type definitions of add and mul. Let us use BSL-expr in analogy for S-expr for the new class of data.

Translate the following expressions into data:
  1. (+ 10 -10)

  2. (+ (* 20 3) 33)

  3. (+ (* 3.14 (* 2 3)) (* 3.14 (* -1 -9)))

InterpretRemember that “interpret” here means “translate from data into information.” In contrast, the word “interpreter” in the title of this chapter refers to a program that consumes the representation of a program and produces its value. While the two ideas are related, they are not the same. the following data as expressions:
  1. (make-add -1 2)

  2. (make-add (make-mul -2 -3) 33)

  3. (make-mul (make-add 1 (make-mul 2 3)) (make-mul 3.14 12))

Now that you have a data representation for BSL programs, it is time to design an evaluator. This function consumes a representation of a BSL expression and produces its value. Again, this function is unlike any you have ever designed so it pays off to experiment with some examples. To this end, you can either use the rules of arithmetic to figure out what the value of an expression is or you can “play” in the interactions area of DrRacket. Take a look at the following table for our examples:

BSL expression

its representation

its value




(+ 1 1)

(make-add 1 1)


(* 3 10)

(make-mul 3 10)


(+ (* 1 1) 10)

(make-add (make-mul 1 1) 10)


Exercise 283. Formulate a data definition for the class of values to which a representation of a BSL expression can evaluate.

Exercise 284. Design eval-expression. The function consumes a representation of a BSL expression (according to exercise 282) and computes its value.

Exercise 285. Develop a data representation for boolean BSL expressions constructed from #true, #false, and, or, and not. Then design eval-bool-expression. The function consumes a representative of boolean BSL expression and computes its value. What is the value of a Boolean expression?

Exercise 286. S-expressions offer a convenient way to express BSL:
> (+ 1 1)


> '(+ 1 1)

(list '+ 1 1)

> (+ (* 3 3) (* 4 4))


> '(+ (* 3 3) (* 4 4))

(list '+ (list '* 3 3) (list '* 4 4))

By simply putting a quote in front of an expression, we get a piece of ISL+ data. This representation is convenient only for the person who types the representation of a BSL expression on a keyboard. Interpreting an S-expression representation is clumsy, mostly because not all S-expressions represent BSL-exprs:
"hello world"
'(+ x 1)
'(* (- "hello" "world") 10)

People invented parsers to check whether some piece of data conforms to a data definition. A parser consumes a “raw” piece of input and, if it does conform, it produces a parse tree, which in our specific case is the corresponding BSL-expr. If not, it signals an error, like the checked functions from Input Errors.

Figure 67 presents a BSL parser for S-expressions. Specifically, the parse function consumes an S-expr and produces an BSL-exprif and only if the given S-expression is the result of quoting a BSL expression that has a BSL-expr representative.

Create test cases for the parse function until DrRacket tells you that all expressions in the definitions area are covered during the test run.

What is unusual about the definition of this program with respect to the design recipe? Note One unusual aspect is that the function uses length on the list argument. Real parsers do not use length because it slows the functions down.

Discuss: should a programming language be designed for the convenience of the programmer who uses it or for the convenience of the programmer who implements it?

(define WRONG "wrong kind of S-expression")
(define-struct add [left right])
(define-struct mul [left right])
; S-expr -> BSL-expr
; creates representation of a BSL expression for s (if possible)
(define (parse s)
  (local (; S-expr -> BSL-expr
          (define (parse s)
              [(atom? s) (parse-atom s)]
              [else (parse-sl s)]))
          ; SL -> BSL-expr
          (define (parse-sl s)
            (local ((define L (length s)))
                [(< L 3)
                 (error WRONG)]
                [(and (= L 3) (symbol? (first s)))
                   [(symbol=? (first s) '+)
                    (make-add (parse (second s)) (parse (third s)))]
                   [(symbol=? (first s) '*)
                    (make-mul (parse (second s)) (parse (third s)))]
                   [else (error WRONG)])]
                 (error WRONG)])))
          ; Atom -> BSL-expr
          (define (parse-atom s)
              [(number? s) s]
              [(string? s) (error "strings not allowed")]
              [(symbol? s) (error "symbols not allowed")])))
    (parse s)))

Figure 67: From S-expr to BSL-expr

24.2 Interpreting Variables

Since the first section ignores constant definitions, an expression does not have a value if it contains a variable. Indeed, if we do not know what x stands for, it makes no sense to evaluate (+ 3 x). Hence, one first refinement of the evaluator is to add variables to the expressions that we wish to evaluate. The assumption is that the definitions area contains a definition such as

(define x 5)

and that someone wants to evaluate expressions containing x in the interactions area:
> x


> (+ x 3)


> (* 1/2 (* x 3))


Indeed, you could imagine a second constant definition, say (define y 3), and interactions that involve two variables:
> (+ (* x x)
     (* y y))


Exercise 286 implicitly proposes symbols as representations for variables. After all, if you were to choose quoted S-expressions to represent expressions with variables, symbols would appear naturally:
> 'x


> '(* 1/2 (* x 3))

(list '* 0.5 (list '* 'x 3))

The obvious alternative is a string, so that "x" would represent x. Other representations are also possible but this book is not about designing evaluators, so we stick with symbols. From this decision, it follows how to modify the data definition from exercise 282:
; A BSL-var-expr is one of:
;  Number
;  Symbol
;  (make-add BSL-var-expr BSL-var-expr)
;  (make-mul BSL-var-expr BSL-var-expr)
We simply add one clause to the data definition.

As for data examples, the following table shows some BSL expressions with variables and their BSL-var-expr representation:

BSL expression

representation of BSL expression



(+ x 3)

(make-add 'x 3)

(* 1/2 (* x 3))

(make-mul 1/2 (make-mul 'x 3))

(+ (* x x)
   (* y y))
(make-add (make-mul 'x 'x)
          (make-mul 'y 'y))
They are all taken from the interactions above, meaning you know the results when x is 5 and y 3.

One way to determine the value of variable expressions is to replace all variables with the values that they represent. This is the way you know from mathematics classes in school, and it is perfectly fine way.

Exercise 287. Design subst. The function consumes a BSL-var-expr e, a Symbol x, and a Number v. It produces a BSL-var-expr like e with all occurrences of x replaced by v.

Exercise 288. Design the function numeric?, which determines whether a BSL-var-expr is also a BSL-expr. Here we assume that your solution to exercise 282 is the definition for BSL-var-expr without the line for Symbol.

Exercise 289. Design eval-variable. The function consumes a BSL-var-expr and determines its value if numeric? is true. Otherwise it signals an error, saying that it is impossible to evaluate an expression that contains a variable.

In general, a program defines many constants in the definitions area and expressions contain more than one variable. To evaluate such expressions, we need a representation of the definition area when it contains a series of constant definitions. For this exercise we use association lists:
; An AL (association list) is [List-of Association].
; An Association is (cons Symbol (cons Number '())).
Make up elements of AL.

Design eval-variable*. The function consumes a BSL-var-expr e and an association list da. Starting from e, it iteratively applies subst to all associations in da. If numeric? holds for the result, it determines its value; otherwise it signals the same error as eval-variable. Hint Think of the given BSL-var-expr as an atomic value and traverse the given association list instead. Or use a loop from figure 55. We provide this hint because the creation of this function requires a little design knowledge from Simultaneous Processing.

Exercise 290. Modify the parser in figure 67 so that it creates BSL-var-expr if it is an appropriate BSL expression.

Exercise 289 relies on the mathematical approach to constant definitions. If a name is defined to stand for some value, then all occurrences of the name can be replaced with the value. Substitution performs this replacement once and for all before the evaluation process even starts.

An alternative approach is to mingle substitution and replacement. That is, the evaluator starts processing the expression immediately but also carries along a the representation of the definitions area. Every time the evaluator encounters a variable, it looks in the definitions area for its value and uses it.

Exercise 291. Design lookup-con. The function consumes an AL da and a Symbol x. It produces the value of x in daif there is a matching Association; otherwise it signals an error.

Exercise 292. Design eval-var-lookup. This function has the same signature as eval-variable*:
; BSL-var-expr AL -> Number
(define (eval-var-lookup e da) ...)
It does not use substitution, however. Instead, the function traverses the expression in the manner that the design recipe for BSL-var-expr suggests and “carries along” da. When it encounters a symbol x, the function looks up the value of x in da.

24.3 Interpreting Functions

At this point, you understand how to evaluate BSL programs that consist of constant definitions and variable expressions. Naturally you want to add function definitions so that you know at least in principle how to deal with the complete programming language. As you may remember from school, people evaluate function applications of the shape (f a) by substituting a for x in e assuming (define (f x) e) is the definition for f. In short, substitution comes in handy again.

The goal of this section is to refine the evaluator of Interpreting Variables so that it can cope with function applications and function definitions. Put differently, we want to design an evaluator with you that simulates DrRacket when the definitions area contains a number of function definitions and a programmer enters an expression in the interactions area that contains applications of these functions.

For simplicity, let us assume that all functions in the definitions area consume one argument. Indeed, for the first two exercises we go further and assume that there is only one function definition.

Exercise 293. Extend the data representation of Interpreting Variables to include the application of a programmer-defined function. Recall that a function application consists of two pieces: a name and an expression. The former is the name of the function that is applied; the latter is the argument.

Use your data definition to represent the following expressions:
  1. (k (+ 1 1))

  2. (* 5 (k (+ 1 1)))

  3. (* (i 5) (k (+ 1 1)))

We refer to this newly defined class of data with BSL-fun-expr.

Exercise 294. Design eval-definition1. The function consumes four arguments:
  1. an BSL-fun-expr e

  2. a symbol f, which represents a function name;

  3. a symbol x, which represents the functions’s parameter; and

  4. an BSL-fun-expr b, which represents the function’s body.

If the terminology poses any difficulties, do re-read BSL: Grammar.

The function determines the value of the expression e. It proceeds as follows. Subexpressions from BSL-expr are evaluated as in exercise 284. For a function application of f,
  1. eval-definition1 evaluates the argument,

  2. it then substitutes the value of the argument for x in b; and

  3. evaluates the resulting expression with eval-definition1.

Here is how to express the steps as code, assuming arg is the argument of the function application:

(eval-definition1 (subst b x (eval-definition1 arg f x b)))

You may have noticed that evaluation of a function application uses a form of recursion that you have not encountered so far. It is dubbed generative recursion and we discuss it in Generative Recursion.

If eval-definition1 encounters a variable, it signals the same error as eval-variable from exercise 289. Also, for function applications that do not refer to f, eval-definition1 signals an error as if it had encountered a variable.

Warning: The use of generative recursion introduces a new element into your computations: non-termination. That is, given some argument, a program may not deliver a result or signal an error but run forever. For fun, you may wish to construct an input for eval-definition1 that causes it to run forever. Use STOP to terminate the program.

For an evaluator that mimics the interaction area, we need a representation of the definitions area. Like in Interpreting Variables, we assume that it is a list of definitions.

Exercise 295. Provide a structure type definition and a data definition for function definitions. Recall that a function definition has three essential attributes:
  1. the function’s name,

  2. the function’s parameter, which is also a name, and

  3. the function’s body, which is a variable expression that usually contains the parameter.

We use BSL-fun-def to refer to the class of data representations for function definitions.

Use your data definition to represent the following BSL function definitions:
  1. (define (f x) (+ 3 x))

  2. (define (g y) (f (* 2 y)))

  3. (define (h v) (+ (f v) (g v)))

Next, define the class BSL-fun-def* to represent definitions area that consist of just one-argument function definitions. Translate the definitions area that defines f, g, and h into your data representation and name it da-fgh.

Finally, design the function lookup-def with the following header:
; BSL-fun-def* Symbol -> BSL-fun-def
; retrieves the definition of f in da
; or signal "undefined function" if da does not contain one
(check-expect (lookup-def da-fgh 'g) g)
(define (lookup-def da f) ...)
Looking up a function definition is needed for the evaluation of expressions in BSL-fun-expr.

Exercise 296. Design eval-function*. The function consumes the BSL-fun-expr representation of some expression e and the BSL-fun-def* representation of a definitions area da. It produces the result that DrRacket shows if you evaluate e in the interactions area assuming the definitions area contains da.

The function works like eval-function1 from exercise 294. For an application of some function f, it
  1. evaluates the argument;

  2. looks up the definition of f in the BSL-fun-def representation of da;

  3. substitutes the value of the argument for the function parameter in the function’s body; and

  4. evaluates the new expression via recursion.

Remember that the representation of a function definition for f comes with a parameter and a body.

Like DrRacket, eval-function* signals an error when it encounters a variable or an application whose function is not defined in the definitions area.

Exercise 297. Modify the parser in figure 67 so that it creates BSL-fun-expr if it is an appropriate BSL expression. Also see exercise 290.

Exercise 298. Figure 68 presents a BSL definitions parser for S-expressions. Specifically, the def-parse function consumes an S-expr and produces an BSL-fun-defif and only if the given S-expression is the result of quoting a BSL definition that has a BSL-fun-def representative.

Create test cases for the def-parse function until DrRacket tells you that all expressions in the definitions area are covered during the test run.

Note The exercises assumes that you have a solution for exercise 297. That is, you have a function parse that turns an S-expr into a BSL-fun-expr representation, if possible.

With def-parse you have the essential ingredient for a parser that consumes an S-expression representation of a definitions area and produces a BSL-fun-def*. Now design the function da-parse, which parses a SL as a BSL-fun-def* assuming the former is a list of quoted BSL definitions.

(define WRONG "wrong kind of S-expression")
(define-struct def [name para body])
; see exercise 295
; S-expr -> BSL-fun-def
; creates representation of a BSL definition for s (if possible)
(define (def-parse s)
  (local (; S-expr -> BSL-fun-def
          (define (def-parse s)
              [(atom? s) (error WRONG)]
               (if (and (= (length s) 3) (eq? (first s) 'define))
                   (head-parse (second s) (parse (third s)))
                   (error WRONG))]))
          ; S-expr BSL-expr -> BSL-fun-def
          (define (head-parse s body)
              [(atom? s) (error WRONG)]
               (if (not (= (length s) 2))
                   (error WRONG)
                   (local ((define name (first s))
                           (define para (second s)))
                     (if (and (symbol? name) (symbol? para))
                         (make-def name para body)
                         (error WRONG))))])))
    (def-parse s)))

Figure 68: From S-expr to BSL-fun-def

24.4 Interpreting Everything

Take a look at the following BSL program:
(define close-to-pi 3.14)
(define (area-of-circle r)
  (* close-to-pi (* r r)))
(define (volume-of-cylinder r h)
  (* h (area-of-circle r)))
Think of these definitions as the definitions area in DrRacket. After you click RUN, you can evaluate expressions involving close-to-pi, area-of-circle, and volume-of-cylinder at the prompt in the interactions area:
> (area-of-circle 1)


> (volume-of-cylinder 1 2)


> (* 3 close-to-pi)


The goal of this section is to refine your evaluator again so that it can mimic this much of DrRacket.

Exercise 299. Formulate a data definition for the representation of DrRacket’s definition area. Concretely, the data representation should work for a sequence that freely mixes constant definitions and one-argument function definitions. Make sure you can represent the definitions area consisting of three definitions at the beginning of this section.—We use BSL-da-all for this class of data.

Design the function lookup-con-def, It consumes a BSL-da-all da and a symbol x. It produces the representation of a constant definition whose name is x, if such a piece of data exists in da; otherwise the function signals an error saying that no such constant definition can be found.

Design the function lookup-fun-def, It consumes a BSL-da-all da and a symbol f. It produces the representation of a function definition whose name is f, if such a piece of data exists in da; otherwise the function signals an error saying that no such function definition can be found.

Exercise 300. Design eval-all. Like eval-function* from exercise 296, this function consumes the representation of an expression and of a definitions area. It produces the same value that DrRacket shows if the expression is entered at the prompt in the interactions area and the definitions area contains the appropriate definitions. Hint Your eval-all function should process variables in the given expression like eval-var-lookup in exercise 292.

Exercise 301. It is cumbersome to enter the structure-based data representation of a BSL expressions and a definitions area. It is much easier to quote an actual expression or a definitions area after surrounding it with parentheses.

Design a function eval-all-sexpr. It consumes an S-expr and an Sl. The former is supposed to represent an expression and the latter a list of definitions. The function parses both with the appropriate parsing functions and then uses eval-all from exercise 300 to evaluate the expression. Hint You must slightly modify da-parse from exercise 298 so that it can parse constant definitions, too.

You should know that eval-all-sexpr makes it straightforward to check whether it really mimics DrRacket’s evaluator.

At this point, you know a lot about interpreting BSL. Here are some of the missing pieces: Booleans with cond or if; Strings and such operations string-length or string-append; and lists with '(), empty?, cons, cons?, first, rest; and so on. Once your evaluator can cope with all these, it is basically complete, because your evaluators already know how to interpret recursive functions. Now when we say “trust us, you know how to design these refinements,” we mean it.

25 The Commerce Of XML

XML is a widely used data language. One use concerns message exchanges between programs running on different computers. For example, when you point your web browser at a web site, you are connecting a program on your computer to a program on another computer, and the latter sends XML data to the former. Once the browser receives the XML data, it renders it as an image on your computer’s monitor.

The following table illustrates this idea with a concrete example:

XML data

rendered in a browser


 <li> hello </li>

 <li> <ul>

       <li> one </li>

       <li> two </li>



 <li> world </li>

 <li> good bye </li>


On the left, you see a piece of XML data that a web site may send to your web browser. On the right, you see how one popular browser renders this snippet graphically.

This chapter explains the basics of processing XMLIf you think XML is too old-fashioned for 2015, remember that this chapter is an exercise. Feel free to re-do the exercise for JSON or an modern data exchange format. The design will remain the same. as another design exercise concerning intertwined data definitions and iterative refinement. XML as S-expressions starts with an informal comparison of S-expressions and XML data and uses it to formulate a full-fledged data definition. The remaining sections explain with examples how to process an S-expression of XML data. Rendering XML Enumerations explains how to render enumerations like the above; Domain-Specific Languages illustrates how to use XML files to create a small language for configuring programs, a common mechanism for modern applications.

25.1 XML as S-expressions

The most basic piece of XML data looks like this:

  <machine> </machine>

It is called an element and “machine” is the name of the element. The two parts of the elements are like parentheses that delimit the content of an element. When there is no content between the two parts—other than white space—XML allows a shorthand:

  <machine />

But as far as we are concerned here, this shorthand is equivalent to the explicit version.

From an S-expression perspective, an XML element is a named pair of parentheses that surround some content.Racket’s xml library represents XML with structures. Although we could use the following structure type to represent an XML element

(define-struct element [name content])

an S-expression representation is even more natural:


This piece of data has the opening and closing parenthesis, and it comes with space to embed content.

Here is a piece of XML data with content:

  <machine><action /></machine>

Remember that the <action /> part is a shorthand, meaning we are really looking at this piece of data:


In general, the content of an XML element is a series of XML elements, e.g.,

  <machine><action /><action /><action /></machine>

Expand the shorthand for <action /> before you continue.

To represent the first nested XML example in ISL+, we can use the element structure type from above:

(make-element "machine" (list (make-element "action" '())))

Since we now know that the content of an element is a sequence of XML data, we naturally use lists of (representations of) XML elements for the content field; '() signals the lack of content. The S-expression alternative looks much simpler than that:

'(machine (action))

When you look at the piece of XML data with a sequence of three <action /> elements as its content, you realize that you may wish to distinguish such elements from each other. To this end, XML elements come with attributes. For example,

  <machine initial="red"></machine>

is the “machine” element equipped with one attribute whose name is “initial” and whose value is “red” between string quotes. Here is complex XML element with nested elements that have attributes too:

  <machine initial="red">

   <action state="red"    next="green" />

   <action state="green"  next="yellow" />

   <action state="yellow" next="red" />


We use blanks, indentation, and line breaks to make the element readable but this white space has no meaning for our XML data here.

The introduction of attributes requires a modification of the structure representation:

(define-struct element [name attributes content])

In addition, a consistently structural representation also calls for an attribute structure type:

(define-struct attribute [name value])

Here we represent attributes as instances of this structure type that combine symbols with strings. A sequence of attributes is a list of such instances. Now it is straightforward to get a piece of ISL+ data for our first machine element:

(make-element 'machine (list (make-attribute 'initial "red")) '())

The complex machine with nested actions corresponds to a nest of structure instances and lists:
(make-element "machine" (list (make-attribute 'initial "red"))
    (make-element "state"
                  (list (make-attribute 'state "red")
                        (make-attribute 'next "green"))
    (make-element "state"
                  (list (make-attribute 'state "green")
                        (make-attribute 'next "yellow"))
    (make-element "state"
                  (list (make-attribute 'state "yellow")
                        (make-attribute 'next "green"))

In contrast, S-expressions for these “machine” elements look much simpler than these two nests of structure instances:

'(machine ((initial "red")))

To add attributes to an element, we use a list of lists where each of the latter contains two items: a symbol and a string. The symbol represents the name of the attribute and the string its value. This idea naturally applies to complex forms of XML data, too:
'(machine ((initial "red"))
          (action ((state "red") (next "green")))
          (action ((state "green") (next "yellow")))
          (action ((state "yellow") (next "red"))))
For now note how the attributes are marked by two opening parentheses and the remaining list of (representations of) XML elements have one opening parenthesis.

You may recall the idea from Intermezzo: Quote, Unquote, which uses S-expressions to represent XHTML, a special dialect of XML. In particular, the intermezzo shows how easily a programmer can write down non-trivial XML data and even templates of XML representations—using backquote and unquote. Of course, the preceding chapter points out that you need a parser to determine whether any given S-expression is a representation of XML data, and a parser is a complex and unusual kind of function.

Nevertheless, we choose to go with a representation of XML based on S-expressions to demonstrate the usefulness of these old ideas in practical terms. Let us proceed gradually to work out a data definition. Here is a first attempt:
; An Xexpr.v0 (short for X-expression) is
;   (cons Symbol '())
This is the “named parentheses” idea from the beginning of this section. Equipping this element representation with content is easy:
; An Xexpr.v1 is
;   (cons Symbol [List-of Xexpr.v1])
The symbolic name becomes the first item on a list that otherwise consists of XML element representatives.

The last refinement step is to add attributes. Since the attributes in an XML element are optional, the revised data definition should have two clauses:
; An Xexpr.v2 is
;  (cons Symbol [List-of Xexpr.v2])
;  (cons Symbol (cons [List-of Attribute] [List-of Xexpr.v2]))
Our sample data representations from above suggest this definition for Attribute:
; An Attribute is
;   (cons Symbol (cons String '()))
The question is whether this data definition is practical for data processing.

Exercise 302. Eliminate the use of List-of from the data definition Xexpr.v2.

Exercise 303. Represent the following XML data as elements of Xexpr.v2:
  1. <transition from="seen-e" to="seen-f" />

  2. <ul><li><word /><word /></li><li><word /></li></ul>

  3. <end></end>

Which one could be represented in Xexpr.v0 or Xexpr.v1?

Exercise 304. Interpret the following elements of Xexpr.v2 as XML data:
  1. '(server ((name "example.org")))

  2. '(carcassonne (board (grass)) (player ((name "sam"))))

  3. '(start)

Which ones are elements of Xexpr.v0 or Xexpr.v1?

Roughly speaking, X-expressions simulate structures via lists. The simulation is convenient for programmers; it asks for the least amount of keyboard typing. For example, if an X-expression does not come with an attribute list, it is simply omitted. This choice of data representation represents a trade-off between authoring such expressions manually and processing them automatically. The best way to deal with the latter problem is to provide functions that make X-expressions look like structures, especially functions that access the quasi-fields:
  • xexpr-name, which extracts the tag of the element representation;

  • xexpr-attributes, which extracts the list of attributes;

  • xexpr-content, which extracts the list of content elements.

Once we have such functions, we can use lists and act as if they were structures.

These functions parse S-expressions, and parsers are tricky to design. So let us follow the design recipe carefully, starting with some data examples:
(define a0 '((initial "red")))
(define e0 '(machine))
(define e1 `(machine ,a0))
(define e2 '(machine (action)))
(define e3 '(machine () (action)))
(define e4 `(machine ,a0 (action) (action)))
The first definition introduces a list of attributes, which is reused twice in the construction of X-expressions. The definition of e0 reminds us that an X-expression may not come with either attributes or content. You should be able to explain why e2 and e3 are basically equivalent.

Next we formulate a signature, a purpose statement, and a header:
; Xexpr.v2 -> [List-of Attribute]
; retrieves the list of attributes of xe
(define (xexpr-attribute xe) '())
Here we focus on xexpr-attribute; we leave the other two functions as exercises.

Making up functional examples requires a decision for X-expressions without attributes, e.g., '(machine) or '(machine (action)). While our chosen representation completely omits missing attributes, we would have to supply '() if we represented the equivalent XML data with structures. We therefore decide that xexpr-attribute produces '() for such X-expressions:
(check-expect (xexpr-attributes e0) '())
(check-expect (xexpr-attributes e1) '((initial "red")))
(check-expect (xexpr-attributes e2) '())
(check-expect (xexpr-attributes e3) '())
(check-expect (xexpr-attributes e4) '((initial "red")))
Before you read on, make sure you can explain all of the examples.

It is time to develop the template. Since the data definition for Xexpr.v2 is complex, we proceed slowly, step by step. First, while the data definition distinguishes two kinds of X-expressions, both clauses describe data constructed by consing a symbol onto a list. Second, what differentiates the two clauses is the rest of the list and especially the optional presence of a list of attributes. Let us translate these two insights into a template:
(define (xexpr-attributes xe)
  (local ((define optional-loa+content (rest xe)))
      [(empty? optional-loa+content) ...]
      [else ...])))
The local definition chops off the name of the X-expression and leaves the remainder of the list, which may or may not start with a list of attributes. The key is that it is just a list, and the two cond clauses indicate so. Third, this list is not defined via a self-reference but as the optional cons of some attributes onto a possibly empty list of X-expressions. In other words, we still need to distinguish the two usual cases and extract the usual pieces:
(define (xexpr-attributes xe)
  (local ((define optional-loa+content (rest xe)))
      [(empty? optional-loa+content) ...]
      [else (... (first optional-loa+content)
             ... (rest optional-loa+content) ...)])))

At this point, we can see that recursion is not needed for the task at hand, and so we switch to the fifth step of the design recipe. Clearly, there are no attributes if the given X-expression comes with nothing but a name. In the second clause, the question is whether the first item on the list is a list of attributes. Since this question involves processing a list, we make a wish:
; design list-of-attributes?
; given: [List-of Attribute] or [List-of Xexpr.v2]
; wanted: #true if it is the first one, #false otherwise
Assuming this function is designed, it is straightforward to finish xexpr-attributes:
(define (xexpr-attributes xe)
  (local ((define optional-loa+content (rest xe)))
      [(empty? optional-loa+content) '()]
      [else (local ((define loa-or-lox (first optional-loa+content)))
              (if (list-of-attributes? loa-or-lox)
If the first item is a list of attributes, the function produces it; otherwise there are no attributes.

For the design of list-of-attributes?, we proceed in the same manner and get this definition:
; [List-of Attribute] or [List-of Xexpr.v2] -> Boolean
; is the given value a list of attributes
(define (list-of-attributes? x)
    [(empty? x) #true]
    [else (local ((define possible-attribute (first x)))
            (cons? possible-attribute))]))
We skip the details of the design process because they are unremarkable. What is remarkable is the signature of this function. Instead of specifying a single data definition as possible inputs, the signatures combines two data definitions separated with the English word “or.” In ISL+... and in the currently popular scripting languages such an informal signature with a definite meaning is acceptable on occasion; do not use it too often, however.

Exercise 305. Design the functions xexpr-name and xexpr-content.

Exercise 306. The design recipe calls for a self-reference in the template for xexpr-attributes. Add this self-reference to the template and then explain why the finished parsing function does not contain it.

Exercise 307. Formulate a data definition that replaces the informal “or” signature for the definition of the list-of-attributes? function.

Exercise 308. Design attribute-value. The function consumes a list of attributes and a symbol. If the attributes list associates the symbol with a string, the function retrieves this string; otherwise it returns #false.—Consider using assq to define the function.

For the remainder of this chapter, we assume that xexpr-name, xexpr-attributes, and xexpr-content exist. Additionally, we also use the attribute-value function from exercise 308. To keep the prose simple, we use Xexpr to refer to Xexpr.v2.

25.2 Rendering XML Enumerations

XML is really a family of languages, similar to the teaching languages in DrRacket, and people define dialects for specific channels of communication. For example, XHTML is the language for sending web in XML format. In this section, we illustrate how to design a rendering function for a small snippet of XHTML, specifically the enumerations from the beginning of this section.

The ul tag surrounds a so-called unordered HTML list. Each item of this list is tagged with li, which tends to contain words but also other elements possibly including enumerations. With “unordered” the authors of HTML express that each item is to be rendered with a leading bullet instead of a number.

Since Xexpr does not come with plain strings, it is not immediately obvious how to represent XHTML enumerations in a subset. One option is to refine the data representation one more time, so that an Xexpr could be a String. Another option is to introduce a representation for text within enumerations:

; A XWord is '(word ((text String))).

In this section, we use this option; Racket, the language from which the teaching languages are derived, offers libraries that equate Xexpr with String.

Exercise 309. Make up three examples of XWords. Design the functions word?, which checks whether any ISL+ value is in XWord, and word-text, which extracts the value of the only attribute of an instance of XWord.

Exercise 310. Refine the definition of Xexpr.v2 so that an you can represent XML elements that are plain strings. Use this refinement to represent enumerations.

Given the representation of words, representing an XHTML-style enumeration of words is straightforward:
; An XEnum.v1 is one of:
;  (cons 'ul [List-of XItem.v1])
;  (cons 'ul (cons [List-of Attribute] [List-of XItem.v1]))
; An XItem.v1 is one of:
;  (cons 'li (cons XWord '()))
;  (cons 'li (cons [List-of Attribute] (cons XWord '())))
The data definition includes attribute lists for completeness, but this section does not take attributes into account for rendering enumerations or items.

Stop! Argue that every element of XEnum.v1 is also in XExpr.

Here is a sample element of XEnum.v1:
(define e0
    (li (word ((text "one"))))
    (li (word ((text "two"))))))
It corresponds to the inner enumeration of the example from the beginning of the chapter. Rendering it with help from the "image" library should yield an image like this:


The radius of the bullet and the distance between the bullet and the text are matters of aesthetic; here the idea matters.

To create this kind image, you might use this ISL+ program:We developed these expressions in the interactions area. What would you do?
(define item1-rendered
  (beside/align 'center BULLET (text "one" 12 'black)))
(define item2-rendered
  (beside/align 'center BULLET (text "two" 12 'black)))
(define e0-rendered
  (above/align 'left item1-rendered item2-rendered))
assuming BULLET is a rendering of a bullet.

But let us design the function carefully. Since the data representation requires two data definitions, the design recipe tells you that you must design two functions in parallel. A second look reveals, however, that in this particular case the second data definition is disconnected from the first one, meaning we can deal with it separately.

Furthermore, the definition for XItem.v1 consists of two clauses, meaning the function itself should consist of a cond with two clauses. The point of viewing XItem.v1 as a sub-language of Xexpr, however, is to think of these two clauses in terms of Xexpr selector functions, in particular, xexpr-content. With this function we can extract the textual part of an item, regardless of whether it comes with attributes or not:
; XItem.v1 -> Image
; renders a single item as a "word" prefixed by a bullet
(define (render-item1 i)
  (... (xexpr-content i) ...))
In general, xexpr-content extracts a list of Xexpr; in this specific case, the list contains exactly one XWord, and this word contains one text:
(define (render-item1 i)
  (local ((define content (xexpr-content i))
          (define element (first content))
          (define word-in-i (word-text element)))
    (... word-in-i ...)))
From here, it is straightforward:
(define (render-item1 i)
  (local ((define content (xexpr-content i))
          (define element (first content))
          (define word-in-i (word-text element)))
    (beside/align 'center BULLET (text word-in-i 12 'black))))
After extracting the text to be rendered in the item, it is simply a question of rendering it as text and equipping it with a leading bullet; see the examples above for how you might discover this last step.

Exercise 311. Before you read on, equip the definition of render-item1 with at least one test; make sure that the tests are formulated so that they don’t truly depend on the nature of BULLET. Then explain how the function works; keep in mind that the purpose statement explains only what it does.

Now we can focus on the design of a function that renders an enumeration. Using the example from above, we can formulate the result of the first two design steps:
; XEnum.v1 -> Image
; renders a simple enumeration as an image
(check-expect (render e0) e0-rendered)
(define (render-enum1 xe)
The key step is the development of a template. According to the data definition, an element of XEnum.v1 contains one interesting piece of data, namely, the (representation of the) XML elements. The first item is always 'ul, so there is no need to extract it, and the second, optional item is a list of attributes, which we ignore. With this in mind, the first template draft looks just like the one for render-item1:
(define (render-enum1 xe)
   (... (xexpr-content xe) ...))
The extracted piece of data is an element of [List-of XItem.v1].

The data-oriented design recipe tells you that you should design a separate function whenever you encounter a complex form of data, such as this list of items. The abstraction-based design recipe from Abstraction tells you to reuse an existing abstraction, say a list-processing function from figure 55, when possible.

Given that render-enum1 is supposed to process a list and create a single image from it, the only two list-processing abstractions whose signatures fit the bill are foldr and foldl. If you also study their purpose statements, you see a pattern that is like the e0-rendered example above, especially for foldr. Let’s try to use it, following the re-use design recipe:
(define (render-enum1 xe)
   (local ((define content (xexpr-content xe))
           ; XItem.v1 Image -> Image
           (define (deal-with-one-item fst-itm so-far)
     (foldr deal-with-one-item empty-image content)))
From the type matching, you also know that:
  1. the first argument to foldr must be a two-argument function, which consumes one item at a time and the image built up so far;

  2. the second argument must be an image;

  3. the last argument is the list.

We consider empty-image the correct starting point because nothing is known about the image; naturally, the XML content is the list of elements to be processed.

This design-by–reuse focuses our attention on one function definition, the function to be “folded” over the list. It turns one item and the image that foldr has created so far into another image. The signature for deal-with-one-item articulates this insight. Since the first argument is an instance of XItem.v1, render-item1 is the function that renders it. This yields two images that must be combined: the image of the first item and the image of the rest of the items. Clearly, deal-with-one-item must stack them on top of each other, which is precisely what above accomplishes:
(define (render-enum1 xe)
  (local ((define content (xexpr-content xe))
          ; XItem.v1 Image -> Image
          (define (deal-with-one-item fst-itm so-far)
            (above/align 'left (render-item1 fst-itm) so-far)))
    (foldr deal-with-one-item empty-image content)))
The example suggests the use of above/align and 'left.

Flat enumerations are common but they are also a simple approximation of the full-fledged case. In the real world, web browsers must cope with nested enumerations that arrive over the web, and at least in principle, the nesting can be arbitrarily deep.Are you wondering whether arbitrary nesting is the correct way to think about this problem? If so, develop a data definition that allows only three levels of enumeration and use it to design rendering functions. In XML and its web browser dialect XHTML, nesting is straightforward. Any element may show up as the content of any other element. To represent this relationship in our limited XHTML representation, we say that an item is either a word or another enumeration:
; An XItem.v2 is one of:
;  (cons 'li (cons XWord '()))
;  (cons 'li (cons [List-of Attribute] (cons XWord '())))
;  (cons 'li (cons XEnum.v2 '()))
;  (cons 'li (cons [List-of Attribute] (cons XEnum.v2 '())))
We must also revise the data definition for enumerations so that they refer to the correct form of item:
; An XEnum.v2 is one of:
;  (cons 'ul [List-of XItem.v2])
;  (cons 'ul (cons [List-of Attribute] [List-of XItem.v2]))

The next question is how this change to the data definition affects the rendering functions. Put differently, how do render-enum1 and render-item1 have to change so that they can cope with elements of XEnum.v2 and XItem.v2, respectively. Software engineers face these kinds of questions all the time, and it is in this situation where the design recipe shines.

(define SIZE 12)
(define COLOR 'black)
(define BULLET
  (beside (circle 1 'solid 'black) (text " " SIZE COLOR)))
; Image -> Image
; marks item with bullet  
(define (bulletize item)
  (beside/align 'center BULLET item))
(define e0 ...)
(define e0-rendered ...)
; XEnum.v2 -> Image
; renders an XEnum.v2 as an image
(check-expect (render-enum e0) e0-rendered)
(define (render-enum an-enum)
  (local ((define content (xexpr-content xe))
          ; XItem.v2 Image -> Image
          (define (deal-with-one-item fst-itm so-far)
            (above/align 'left (render-item1 fst-itm) so-far)))
    (foldr deal-with-one-item empty-image content)))
; XItem.v2 -> Image
; renders one XItem.v2 as an image
  (render-item '(li (word ((text "one")))))
  (bulletize (text "one" SIZE COLOR)))
(check-expect (render-item `(li ,e0)) (bulletize e0-rendered))
(define (render-item an-item)
  (local ((define content (first (xexpr-content an-item))))
     'center BULLET
       [(word? content) (text (word-text content) FTSZ 'black)]
       [else (render-enum content)]))))

Figure 69: Refining functions in response to refinements of data definitions

Figure 69 shows the complete answer. Since the change is confined to the data definitions for XItem.v2, it should not come as a surprise that the change to the rendering program shows up in the function for rendering items. While render-item1 does not need to distinguish between different forms of XItem.v1, render-item is forced to use a cond because XItem.v2 lists two different kinds of items. Given that this data definition is close to one from the real world, the distinguishing characteristic is not something simple—like '() vs consbut a specific piece of the given item. If the item’s content is a Word, the rendering function proceeds as before. Otherwise, the item contains an enumeration, in which case render-item uses render-enum to deal with the data, because the data definition for XItem.v2 refers back to XEnum.v2 precisely at this point.

Exercise 312. Use the recipe to design the rendering functions for XEnum.v2 and XItem.v2 from scratch. You should come up with the same functions.

Exercise 313. The wrapping of cond with (beside/align 'center BULLET ...) may surprise you. Edit the function definition so that the wrap-around appears once in each clause. Why are you confident that your change works? Which version do you prefer?

Exercise 314. Design a program that counts all occurrences of "hello" in an instance of XEnum.v2.

Exercise 315. Design a program that replaces all occurrences of "hello" with "bye" in an enumeration.

25.3 Domain-Specific Languages

Engineers routinely build large software systems that require a configuration for specific contexts before they can be run. This configuration task tends to fall to systems administrators who must deal with many different software systems. The word “configuration” refers to the data that the main function needs when the program is launched. In a sense a configuration is just an addition argument, though it is usually so complex that program designers prefer a different mechanism for handing it over.

Since software engineers cannot assume that systems administrators know every programming language, they tend to device simple, special-purpose configuration languages. These special language are also known as a domain-specific languages (DSL).Because configurations abstract a program over various pieces of data, Prof. Paul Hudak argued in the 1990s that DSLs are the ultimate abstractions, that is, that they generalize the ideas of Abstraction to perfection. Developing these DSLs around a common core, say the well-known XML syntax, simplifies life for systems administrators. They can write small XML “programs” and thus configure the systems they must launch.

While the construction of a DSL is often considered a task for an advanced programmer, you are actually in a position to understand, appreciate, and implement a reasonably complex DSL already. This section explains how it all works. It first re-acquaints you with finite state machines (FSMs). Then it shows how to design, implement, and program a DSL for configuring a system that simulates arbitrary FSMs.

Finite State Machines Remembered The theme of finite state machine is an important one in computing and this book has presented it several times already, starting with A Bit More about Worlds and especially exercise 100 through Finite State Machines and ... Add Expressive Power. Here we reuse the example from the last section as the component for which we wish to design and implement a configuration DSL.

; A FSM is a [List-of 1Transition]
; A 1Transition is a list of two items:
;   (cons FSM-State (cons FSM-State '()))
; A FSM-State is a String that specifies color
; data examples
(define fsm-traffic
  '(("red" "green") ("green" "yellow") ("yellow" "red")))
; FSM FSM-State -> FSM-State
; match the keys pressed by a player with the given FSM
(define (simulate state0 transitions)
  ; State of the World: FSM-State
  (big-bang state0
             (lambda (current)
               (square 100 "solid" current))]
             (lambda (current key-event)
               (find transitions current))]))
; [List-of (cons X (cons Y '()))] X -> Y
; finds the matching Y for the given X in the association list
(define (find alist x)
  (local ((define fm (assoc x alist)))
    (if (cons? fm) (second fm) (error "next state not found"))))

Figure 70: Finite state machines, revisited

For convenience, figure 70 presents the entire code again, though reformulated using just lists and using the full power of ISL+. The program consists of two data definitions, one data example, and two function definitions: simulate and find. Unlike the related programs in preceding chapters, this one represents a transition as a list of two items: the current state and the next one.

The main function, simulate, consumes a transition table and an initial state; it then evaluates a big-bang expression, which reacts to each key event with a state transition. The states are displayed as colored squares. The to-draw and on-key clauses are specified with lambda expressions that consume the current state, plus the actual key event, and that produce an image or the next state, respectively.

As its signature shows, the auxiliary find function is completely independent of the FSM application. It consumes a list of two-item lists and an item but the actual nature of the items is specified via parameters. In the context of this program, X and Y represent FSM-States, meaning find consumes a transition table together with a state and produces a state. The function body uses the built-in assoc function to perform most of the work. Look up the documentation for assoc so that you understand why the body of local uses an if expression.

Exercise 316. Modify the rendering function so that it overlays the name of the state onto the colored square.

Exercise 317. Formulate test cases for find. Design find from scratch.

Exercise 318. Reformulate the data definition for 1Transition so that it is possible to restrict transitions to certain key strokes. Try to formulate the change so that find continues to work without change. What else do you need to change to get the complete program to work? Which part of the design recipe provides the answer(s)? See exercise 196 for the original exercise statement.

Configurations The FSM simulation program requires two arguments, which jointly describe a machine. Rather than teach a potential “customer” how to open a ISL+ program in DrRacket and launch a function of two arguments, the “seller” of simulate may wish to supplement this product with a configuration component.

A configuration component consists of two parts. The first one is a widely used simple language that customers use to formulate the initial arguments for a component’s main function(s). The second one is a function that translates what customers say into a function call for the main function. For the FSM simulator, we must agree on how we represent finite state machines in XML. By judicious planning, XML as S-expressions presents a series of machine examples that look just right for the task. Recall the final machine example in this section:

  <machine initial="red">

   <action state="red"    next="green" />

   <action state="green"  next="yellow" />

   <action state="yellow" next="red" />


Compare it to the transition table fsm-traffic from figure 70. Also recall the agreed-upon Xexpr representation of this example:
(define xm0
  '(machine ((initial "red"))
            (action ((state "red") (next "green")))
            (action ((state "green") (next "yellow")))
            (action ((state "yellow") (next "red")))))

What we are still lacking is a general data definition that describes all possible Xexpr representations of finite state machines:
; An XMachine is:
;   (list 'machine (list (list 'initial FSM-State)) [List-of X1T])
; An X1T is
;   (list 'action (list (list 'state FSM-State) (list 'next FSM-State)))
Like XEnum.v2, XMachine describes a subset of all Xexpr. Thus, when we design functions that process this new form of data, we may continue to use the generic Xexpr functions to access pieces.

Exercise 319. Formulate an XML configuration for the BW Machine from exercise 194 and translate it into an XMachine representation.

Before we dive into the translation part of the configuration problem, let’s spell it out:

Sample Problem: Design a program that uses an XMachine configuration to run simulate.

While this problem is specific to our case, it is easy to imagine a generalization for similar systems and we encourage you to do so.

The problem statement implies a signature, a purpose statement, and a header:
; XMachine -> FSM-State
; simulates an FSM via the given configuration
(define (simulate-xmachine xm)
  (simulate ... ...))
Indeed, it almost dictates the complete function definition. Following the problem statement, our function calls simulate with two to-be-determined arguments. What we need to complete the definition are two pieces: an initial state and a transition table. These two pieces are part of xm and we are best off wishing for appropriate functions:

; XMachine -> FSM-State
; interprets the given configuration as a state machine
(define (simulate-xmachine xm)
  (simulate (xm->transitions xm) (xm-state0 xm)))
; XMachine -> FSM-State
; extracts and translate the transition table from a configuration
(check-expect (xm-state0 xm0) "red")
(define (xm-state0 xm0)
  (attribute-value (xexpr-attributes xm0) 'initial))
; XMachine -> [List-of 1Transition]
; extracts the transition table from an XML configuration
(check-expect (xm->transitions xm0) fsm-traffic)
(define (xm->transitions xm)
  (local (; X1T -> 1Transition
          (define (xaction->action xa)
            (list (attribute-value (xexpr-attributes xa) 'state)
                  (attribute-value (xexpr-attributes xa) 'next))))
    (map xaction->action (xexpr-content xm))))

Figure 71: Interpreting a DSL program

Since XMachine is a subset of Xexpr, defining xm-state0 is straightforward. Given that the initial state is specified as an attribute, xm-state0 extracts the list of attributes using xexpr-attributes and then retrieves the value of the 'initial attribute.

Let us then turn to xm->transitions, which translates the transitions inside of an XMachine configuration into a transition table:
; XMachine -> [List-of 1Transition]
; extracts and translates the transition table from a configuration
(define (xm->transitions xm)
The name of the function prescribes the signature and suggests a purpose statement. Our purpose statement describes a two-step process: (1) extract the Xexpr representation of the transitions and (2) translate them into an instance of [List-of 1Transition].

While the extraction part obviously uses xexpr-content to get the list, the translation part calls for some more analysis. If you look back to the data definition of XMachine, you see that the content of the Xexpr is a list of X1Ts. The signature tells us that the transition table is a list of 1Transitions. Indeed, it is quite obvious that each item in the former list is translated into one item of the latter, which suggests we used map to define the function:
(define (xm->transitions xm)
  (local (; X1T -> 1Transition
          ; translates an Xexpr transition into a list
          (define (xaction->action xa)
    (map xaction->action (xexpr-content xm))))
As you can see, we follow the design ideas of Using Abstractions, by Examples and formulate the function as a local whose body uses map. Defining xaction->action is again just a matter of extracting the appropriate values from an Xexpr.

Figure 71 displays the complete definitions for all three functions: simulate-xmachine, xm-state0, and xm->transitions. In this case, the translation from the DSL to a proper function call is as large as the original component. This is not the case for real-world systems; the DSL component tends to be a small fraction of the overall product, which is why the approach is so popular.

Exercise 320. Run the code in figure 71 with the BW Machine configuration from exercise 319.

25.4 Reading XML

This section requires the "batch-io" library. Systems administrators expect that systems configuration systems read configuration programs from a file or possibly from some place on the web. In ISL+ your programs can retrieve (some) XML information from files and the web with the help of the "batch-io" library. Figure 72 shows the relevant excerpt from teachpack.

; Xexpr.v3 is one of:
;  Symbol
;  String
;  Number
;  (cons Symbol (cons Attribute*.v3 [List-of Xexpr.v3]))
;  (cons Symbol [List-of Xexpr.v3])
; Attribute*.v3 is [List-of Attribute.v3]
; Attribute.v3 is:
;   (list Symbol String)
; interpretation.: (list 'a "some text") represents a="some text"
; Any -> Boolean
; is the given value an Xexpr.v3
; effect display bad piece if x is not an Xexpr.v3
(define (xexpr? x) ...)
; String -> Xexpr.v3
; produces the first XML element in file f as an X-expression
(define (read-xexpr f) ...)
; String -> Xexpr.v3
; produces the first XML element in file f as an X-expression
; and all whitespace between embedded elements is eliminated  
; assume the XML element may not contain any text as elements
(define (read-plain-xexpr f) ...)
; String -> Boolean
; #false, if this url returns a '404'; #true otherwise
(define (url-exists? u) ...)
; String -> [Maybe Xexpr.v3]
; produces the first XML element from URL u as an X-expression
; or #false if (not (url-exists? u))
; reads HTML as XML if possible
; effect signals an error in case of network problems
(define (read-xexpr/web u) ...)
; String -> [Maybe Xexpr.v3]
; produces the first XML element from URL u as an X-expression
; and all whitespace between embedded elements is eliminated  
; or #false if (not (url-exists? u))
; reads HTML as XML if possible
; effect signals an error in case of network problems
(define (read-plain-xexpr/web u) ...)
; Xexpr.v3 -> String
; renders the X-expression x as a string
(define (xexpr-as-string x) ...)

Figure 72: Reading X-expressions

For illustrative purposes, assume we have a file called "machine-configuration.xml" and that it contains the following XML element:


<machine initial="red">

 <action state="red"    next="green" />

 <action state="green"  next="yellow" />

 <action state="yellow" next="red" />


If a program uses the "batch-io" library, it can read the element in one of two ways. First, it may use read-plain-xexpr:
> (read-plain-xexpr "machine-configuration.xml")

read-plain-xexpr: file "machine-configuration.xml" not found in "/Users/matthias/svn/2HtDP/" [the program's folder]

Second, it may use read-xexpr:
> (read-xexpr "machine-configuration.xml")

read-xexpr: file "machine-configuration.xml" not found in "/Users/matthias/svn/2HtDP/" [the program's folder]

The difference is that the first retrieves the XML element in a format that matches the XMachine data definition while the second preserves the line breaks and other white space in the file.

Both forms of reading XML elements are useful though in the context of a configuration sub-system only the first one is needed. If you are processing an XML element for text, however, you may wish to use the second form.

Similar mechanisms for retrieving XML elements from the web are also available in the teachpack. Try the following interaction in your DrRacket:
> (read-plain-xexpr/web
If your computer is connected to the web, this expression retrieves our standard machine configuration.

Reading files or web pages introduces an entirely novel idea into our computational model. As Intermezzo: BSL explains, a BSL program is evaluated in the same manner in which you evaluate variable expressions in algebra. Function definitions are also treated just like in algebra. Indeed, most algebra courses introduce conditional function definition, meaning cond and if do not pose any new challenges either. Finally, while ISL introduces functions as values—with ISL+ making this truly straightforward—the evaluation model remains fundamentally the same.

One essential property of this computational model is that no matter how often you call a function f on some argument(s) a ...

(f a ...)

the answer remains the same. The introduction of read-file, read-xexpr, and their relatives destroys this property, however. The problem is that files and web sites may change over time so that every time a program reads files or web sites it may get a new result.

Consider the idea of looking up the stock price of a company. Point your browser to google.com/finance or any other such financial web site and enter the name of your favorite company, say, Ford. In response, the site will display the current price of the company’s stock and other information, for example, how much the price has changed since the last time it was posted, the current time, and many other facts and ads. The important point is that as you reload this page over the course of a day or a week, some of the information on this web page will change.

An alternative to looking up such company information manually is to write a small program that retrieves such information on a regular basis, say, every 15 seconds. With ISL you can write a world program that performs this task. You would launch it like this:
> (stock-alert "Ford")
and you might inspect that the world window displays an image like the following:


To develop such a program requires skills beyond normal program design. First, you need to investigate how the web site formats its information. In the case of Google’s financial service page, an inspection of the web source code shows the following pattern near the top:

  <meta content="17.09" itemprop="price" />

  <meta content="+0.07" itemprop="priceChange" />

  <meta content="0.41" itemprop="priceChangePercent" />

  <meta content="2013-08-12T16:59:06Z" itemprop="quoteTime" />

  <meta content="NYSE real-time data" itemprop="dataSource" />

If we had a function that could search an Xexpr.v3 and extract (the representation of XML) meta elements with the attribute value "price" and "priceChange", the rest of stock-alert would be straightforward.

Figure 73 displays the core of the program. As the figure shows, the main function defines two local ones: a clock tick handler and a rendering function. The big-bang specification requests that the clock tick every 15 seconds. When the clock ticks, ISL+ applies retrieve-stock-data to the current world and the function ignores it. Instead of using its argument, the function visits the web site via read-xexpr/web and then extracts the appropriate information with get. In other words, the new world is created from newly available information on the web not some locally available data.

The design of get is left to the exercises, because its workings are all about processing intertwined data.

(require 2htdp/universe)
(require 2htdp/image)
(require 2htdp/batch-io)
(define PREFIX "https://www.google.com/finance?q=")
(define SUFFIX "&btnG=Search")
(define SIZE 22)
(define SPACER (text "  " SIZE 'white))
(define-struct data [price delta])
; StockWorld is
;    (make-data String String)
; price and delta specify the current price and how
; much it changed since the last update
; String -> StockWorld
; retrieves stock price and its change of the specified company
; every 15 seconds and displays together with available time stamp
(define (stock-alert company)
  (local ((define url (string-append PREFIX company SUFFIX))
          ; [StockWorld -> StockWorld]
          ; retrieves price, change, and time from url
          (define (retrieve-stock-data __w)
            (local ((define x (read-xexpr/web url)))
              (make-data (get x "price") (get x "priceChange"))))
          ; StockWorld -> Image
          ; renders the stock market data as a single long line
          (define (render-stock-data w)
            (local ((define pt (text (data-price w) SIZE 'black))
                    (define dt (text (data-delta w) SIZE 'red)))
              (overlay (beside pt SPACER dt)
                       (rectangle 300 35 'solid 'white)))))
    ;  IN
    (big-bang (retrieve-stock-data 'no-use)
              [on-tick retrieve-stock-data 15]
              [to-draw render-stock-data]
              [name company])))

Figure 73: Web data as an event

Exercise 321. Look up the current stock price for your favorite company at Google’s financial service page. If you don’t favor a company, pick Ford. Then save the source code of the page as a file in your working directory. Use read-xexpr in DrRacket to view the source as an Xexpr.v3.

Exercise 322. Here is the get function missing from figure 73:
; Xexpr.v3 String -> String
; retrieves the value of the "content" attribute for
; a 'meta element with attribute "itemprop" and value s
  (get '(meta ((content "+0.11") (itemprop "delta"))) "delta")
  (get '(meta ((itemprop "price") (content "17.01"))) "price")
  (get '(meta ((itemprop "price") (content "17.01"))) "delta")
  "attribute not found: delta")
(define (get x s)
  (local ((define result (get-xexpr x s)))
    (if (string? result)
        (error (string-append "attribute not found: " s)))))
It assumes the existence of a get-xexpr function that searches an arbitrary Xexpr.v3 for the desired attribute and produces [Maybe String].

Design get-xexpr. Derive functional examples for this function from those for get. Generalize these examples so that you are confident get-xexpr can traverse an arbitrary Xexpr.v3. Finally, formulate a test that uses the web data saved in exercise 321.

26 Simultaneous Processing

Some functions have to consume two arguments that belong to classes with non-trivial data definitions. How to design of such functions depends on the relationship between the arguments. First, one of the arguments may have to be treated as if it were atomic. Second, it is possible that the function must process the two arguments in lockstep. Finally, the function may process the given data in accordance to all possible cases. This chapter illustrates the three cases with examples and provides an augmented design recipe. The last section discusses the equality of compound data.

26.1 Processing Two Lists Simultaneously: Case 1

Consider the following signature, purpose statement, and header:
; [List-of Number] [List-of Number] -> [List-of Number]
; constructs a new list by replacing '() in front with end
(define (replace-eol-with front end)
The signature says that the function consumes two lists, which we have not so far. Let’s see how the design recipe works in this case.

To start with, we make up examples. If the first argument is '(), replace-eol-with is to produce the second argument, no matter what it is:

(check-expect (replace-eol-with '() '(a b c)) '(a b c))

In contrast, if the first argument is not '(), the purpose statement requires that we replace '() at the end of front with end:
(check-expect (replace-eol-with (cons 1 '()) '(a)) (cons 1 '(a)))
(check-expect (replace-eol-with (cons 2 (cons 1 '())) '(a))
              (cons 2 (cons 1 '(a))))

The purpose statement and the examples suggest that as long as the second argument is a list, the function does not need to know anything about it. By implication, its template should be that of a list-processing function with respect to the first argument:
(define (replace-eol-with front end)
    [(empty? front) ...]
    [else (... (first front) ...
           ... (replace-eol-with (rest front) end) ...)]))

Let’s fill the gaps in the template following the fifth step of the design recipe. If front is '(), replace-eol-with produces end according to our examples. If front is not '(), we must recall what the template expressions compute:
  • (first front) evaluates to the first item on the list, and

  • (replace-eol-with (rest front) end) replaces '() in (rest front) with end.

To understand what these bullets mean, consider the last example where (first front) is 1 and (rest front) is (cons 1 '()). According to the purpose statement, (replace-eol-with (rest front) end) is supposed to be (cons 2 (cons 1 '(a))). To obtain the desired result, the function can cons 2 onto (cons 1 '(a)).

By generalizing the insight from the last step, we get the complete function definition:
; [List-of Number] [List-of Number] -> [List-of Number]
; constructs a new list by replacing '() in front with end
(define (replace-eol-with front end)
    [(empty? front) end]
    [else (cons (first front)
                (replace-eol-with (rest front) end))]))

Exercise 323. Design cross. The function consumes a list of symbols and a list of numbers and produces all possible ordered pairs of symbols and numbers. That is, when given '(a b c) and '(1 2), the expected result is '((a 1) (a 2) (b 1) (b 2) (c 1) (c 2)).

26.2 Processing Two Lists Simultaneously: Case 2

Functions that Produce Lists presents the function wages*, which computes the weekly wages of a some workers given their work hours. It consumes a list of numbers—representing the hours worked per week—and produces a list of numbers—the corresponding weekly wages. While the problem assumes that all employees received the same pay rate, even a small company pays its workers differentiated wages.

Here we look at a slightly more realistic version. The function now consumes two lists: the list of hours worked and the list of corresponding hourly wages. We translate this revised problem into a revised signature and purpose statement:
; [List-of Number] [List-of Number] -> [List-of Number]
; computes weekly wages by multiplying the corresponding
; items on hours and hourly-wages
; assume the two lists are of equal length
(define (wages*.v2 hours hourly-wages)
Making up examples is straightforward:
(check-expect (wages*.v2 '() '()) '())
(check-expect (wages*.v2 (list 5.65) (list 40)) (list 226.0))
(check-expect (wages*.v2 '(5.65 8.75) '(40.0 30.0)) '(226.0 262.5))
As required by the purpose statement, all three examples use lists of equal length.

The assumption concerning the inputs can also be exploited for the development of the template. More concretely, the condition says that (empty? hours) is true precisely when (empty? hourly-wages) is true; and furthermore, (cons? hours) is true when (cons? hourly-wages) is true. It is therefore acceptable to use a template that focuses on either one of the two lists:
(define (wages*.v2 hours hourly-wages)
    [(empty? hours) ...]
      (... (first hours) ... (first hourly-wages) ...
       ... (wages*.v2 (rest hours) (rest hourly-wages)) ...)]))
In the first cond clause, both hours and hourly-wages are '(). Hence no selector expressions are needed. In the second clause, both hours and hourly-wages are constructed lists, which means we need four selector expressions: (first hours), (rest hours), (first hourly-wages) and (rest hourly-wages). Finally, because the last two are lists of equal length, they make up a natural candidate for the natural recursion of wages*.v2.

The only unusual aspect of this template is that the recursive application consists of two expressions, both selector expressions for the two arguments. But, this idea directly follows from the assumption.

From here, it is a short step to a complete function definition:
(define (wages*.v2 hours hourly-wages)
    [(empty? hours) '()]
    [else (cons (weekly-wage (first hours) (first hourly-wages))
                (wages*.v2 (rest hours) (rest hourly-wages)))]))
The first example implies that the answer for the first cond clause is '(). In the second one, we have three values available:
  1. (first hours), which represents the first item on the list of weekly hours;

  2. (first hourly-wages), which is the first item on the list of pay rates; and

  3. (wages*.v2 (rest hours) (rest hourly-wages)), which according to the purpose statement, computes the list of weekly wages for the remainders of the two lists.

Now we just need to combine these values to get the final answer. As suggested by the examples, we must compute the weekly wage for the first employee and construct a list from that wage and the rest of the wages:
(cons (weekly-wage (first hours) (first hourly-wages))
      (wages*.v2 (rest hours) (rest hourly-wages)))
The auxiliary function weekly-wage consumes the number of hours worked and the pay rate to compute the weekly wage for one worker:
; Number Number -> Number
; computes the weekly wage from pay-rate and hours-worked
(define (weekly-wage pay-rate hours-worked)
  (* pay-rate hours-worked))
Stop! Which function do you need to use if you wish to compute the wages for one worker? Which function do you need to change if you wish to deal with income taxes?

Exercise 324. In the real world, wages*.v2 consumes lists of employee structures and lists of work records. An employee structure contains an employee’s name, social security number, and pay rate. A work record also contains an employee’s name and the number of hours worked in a week. The result is a list of structures that contain the name of the employee and the weekly wage.

Modify the program in this section so that it works on these realistic versions of data. Provide the necessary structure type definitions and data definitions. Use the design recipe to guide the modification process.

Exercise 325. Design the zip function, which consumes a list of names, represented as strings, and a list phone numbers, also strings. It combines those equally long lists into a list of phone records:
(define-struct phone-record [name number])
; A PhoneRecord is (make-phone-record String String).
The assumption is that corresponding list items belong to the same person.

26.3 Processing Two Lists Simultaneously: Case 3

Here is a third type of problem:

Sample Problem: Given a list of symbols los and a natural number n, the function list-pick extracts the nth symbol from los; if there is no such symbol, it signals an error.

The question is how systematically the recipe works for the design of list-pick.

While the data definition for a list of symbols is fairly familiar by now, recall from Natural Numbers that natural numbers are also defined with a self-referential data definition:
; N is one of:
;  0
;  (add1 N)
And you know the examples: 0, 1, 2, 3.

Now we can proceed to the second step, stating a function signature, purpose statement, and header:
; [List-of Symbol] N -> Symbol
; extracts the nth symbol from l;
; signals an error if there is no such symbol
(define (list-pick l n)
Both lists of symbols and natural numbers are classes with complex data definitions. This combination makes the problem non-standard, meaning we must pay attention to every detail for every step of the design recipe.

At this point, we usually pick some input examples and figure out what the desired output is. We start with inputs for which the function has to work flawlessly: '(a b c) and 2. For a list of three symbols and the index 2, list-pick must return a symbol. The question is whether it is 'b or 'c. In grade school, you would have counted 1, 2, and picked 'b without a first thought. But this is computer science, not grade school. Here people start counting from 0 meaning that 'c is an equally appropriate choice. And indeed, this is the choice we will use:

(check-expect (list-pick '(a b c) 2) 'c)

Now that we have eliminated this fine point of list-pick, let’s look at the actual problem, the choice of inputs. The goal of the example step is to cover the input space as much as possible. We do so by picking one input per clause in the description of complex forms of data. Here this procedure suggests we pick at least two elements from each class because each data definition has two clauses. We choose '() and (cons 'a '()) for the first argument, and 0 and 3 for the latter. Two choices per argument means four examples total; after all, there is no immediately obvious connection between the two arguments and no restriction in the signature.

As it turns out, only one of these pairings produces a proper result, the remaining ones choose a position that does not exist because the list doesn’t contain enough symbols:
(check-error (list-pick '() 0) "list too short")
(check-expect (list-pick (cons 'a '()) 0) 'a)
(check-error (list-pick '() 3) "list too short")
(check-error (list-pick (cons 'a '()) 3) "list too short")
The function is expected to signal an error, and we pick our favorite message here.

Stop! Put these fragments into DrRacket’s definition area and run the partial program.

The discussion on examples indicates that there are indeed four independent cases that we must inspect for the design of the function. One way to discover these cases is to arrange the conditions for each of the clauses into a two-dimensional table:

(empty? l)

(cons? l)

(= n 0)

(> n 0)

The horizontal dimension of the table lists those questions that list-pick must ask about lists; the vertical dimension lists the questions about natural numbers. By this arrangement, we naturally get four squares, where each represents the case when both the condition on the horizontal and the vertical axis are true.

Our table suggests that the cond for the function template has four clauses. We can figure out the appropriate condition for each of these clauses by and-ing the horizontal and vertical condition for each box in the table:

(empty? l)

(cons? l)

(= n 0)

(and (empty? l) (= n 0))

(and (cons? l) (= n 0))

(> n 0)

(and (empty? l) (> n 0))

(and (cons? l) (> n 0))

The cond outline of the template is merely a translation of this table into a conditional:
(define (list-pick l n)
    [(and (= n 0) (empty? l)) ...]
    [(and (> n 0) (empty? l)) ...]
    [(and (= n 0) (cons? l)) ...]
    [(and (> n 0) (cons? l)) ...]))

As always, the cond expression allows us to distinguish the four possibilities and to focus on each individually as we add selector expressions to each cond clause:
(define (list-pick l n)
    [(and (= n 0) (empty? l))
    [(and (> n 0) (empty? l))
     (... (sub1 n) ...)]
    [(and (= n 0) (cons? l))
     (... (first l) ... (rest l)...)]
    [(and (> n 0) (cons? l))
     (... (sub1 n) ... (first l) ... (rest l) ...)]))
The first argument, l, is a list and we know that cond clauses for non-empty lists contain two selector expressions in a template. The second argument, n, is a natural number and the self-referential clause its data definition says that we need only one selector expression—when (> n 0). In those cases where either (= n 0) or (empty? l) holds, the corresponding argument is atomic and there is no need for a selector expression.

The final step of the template construction demands that we annotate the template with recursions where the results of selector expressions belong to the same class as the inputs. For this first example, we focus on the last cond clause, which contains selector expressions for both arguments. It is, however, unclear how to form the natural recursions. If we disregard the purpose of the function, there are three possible recursions:
  1. (list-pick (rest l) (sub1 n))

  2. (list-pick l (sub1 n))

  3. (list-pick (rest l) n)

Each one represents a feasible combination of the available expressions. Since we cannot know which one matters or whether all three matter, we move on to the next development stage.

; [List-of Symbol] N -> Symbol
; extracts the nth symbol from l;
; signals an error if there is no such symbol
(define (list-pick l n)
    [(and (= n 0) (empty? l))
     (error 'list-pick "list too short")]
    [(and (> n 0) (empty? l))
     (error 'list-pick "list too short")]
    [(and (= n 0) (cons? l)) (first l)]
    [(and (> n 0) (cons? l)) (list-pick (rest l) (sub1 n))]))

Figure 74: Indexing into a list

Following the design recipe for the fifth step, let us analyze each cond clause in the template and decide what a proper answer is:
  1. If (and (= n 0) (empty? l)) holds, list-pick is asked to pick the first symbol from an empty list, which is impossible. The answer must be an error signal.

  2. If (and (> n 0) (empty? l)) holds, list-pick is again asked to pick an symbol from an empty list.

  3. If (and (= n 0) (cons? l)) holds, list-pick is supposed to produce the first symbol from l. The selector expression (first l) is the answer.

  4. If (and (> n 0) (cons? l)) holds, we must analyze what the available expressions compute. As we have seen, it is a good idea to work through an existing example for this step. We pick a shortened variant of the first example:

    (check-expect (list-pick '(a b) 1) 'b)

    Here is what the three natural recursions compute with these values:
    1. (list-pick '(b) 0) produces 'b;

    2. (list-pick '(a b) 0) evaluates to 'a, which is the wrong answer; and

    3. (list-pick '(b) 1) signals an error because the index is too large.

    From this, we conclude that (list-pick (rest l) (sub1 n)) computes the desired answer in the last cond clause.

Exercise 326. Design the function tree-pick. The function consumes a tree of symbols and a list of directions:
(define-struct branch [left right])
; A TOS is one of:
;  Symbol
;  (make-branch TOS TOS)
; A Direction is one of:
;  'left
;  'right
; A list of Directions is also called a path.
Clearly a Direction tells the function whether to choose the left or the right branch in a non-symbolic tree. The function signals an error when it is given a symbol and a non-empty path.

26.4 Function Simplification

The list-pick function in figure 74 is far more complicated than necessary. The first two cond clauses signal an error. That is, if either

(and (= n 1) (empty? alos))


(and (> n 1) (empty? alos))

holds, the answer is an error. We can translate this observation into code:
(define (list-pick alos n)
    [(or (and (= n 1) (empty? alos))
         (and (> n 1) (empty? alos)))
     (error 'list-pick "list too short")]
    [(and (= n 1) (cons? alos)) (first alos)]
    [(and (> n 1) (cons? alos)) (list-pick (rest alos) (sub1 n))]))

To simplify this function even more, we need to get acquainted with an algebraic law concerning Booleans:

(or (and bexp1 a-bexp)
    (and bexp2 a-bexp))


(and (or bexp1 bexp2)

The equation, due to de Morgan, is called the law of distributivity. A similar law holds when the first two conditions in the and expressions are the same and the second ones differ. Applying de Morgan’s law to list-pick yields
(define (list-pick n alos)
    [(and (or (= n 1) (> n 1))
          (empty? alos))
     (error 'list-pick "list too short")]
    [(and (= n 1) (cons? alos)) (first alos)]
    [(and (> n 1) (cons? alos)) (list-pick (rest alos) (sub1 n))]))

Now consider (or (= n 1) (> n 1)), the first part of the condition. Since n belongs to N, the condition is always true. But if we replace it with #true, we get (and #true (empty? alos)), which is clearly equivalent to (empty? alos). In other words, the function can be rewritten as
(define (list-pick alos n)
    [(empty? alos) (error 'list-pick "list too short")]
    [(and (= n 1) (cons? alos)) (first alos)]
    [(and (> n 1) (cons? alos)) (list-pick (rest alos) (sub1 n))]))
which is already significantly simpler than the definition in figure 74.

We can do even better than this. Compare the first condition in the latest version of list-pick with the second and third. Since the first cond clause filters out all those cases when alos is empty, (cons? alos) in the last two clauses is always going to evaluate to #true. If we replace the condition with #true and simplify the and expressions again, we get a three-line version of list-pick

; list-pick: [List-of Symbol] N[>= 1] -> Symbol
; determines the nth symbol from alos, counting from 1;
; signals an error if there is no nth symbol
(define (list-pick alos n)
    [(empty? alos) (error 'list-pick "list too short")]
    [(= n 1) (first alos)]
    [(> n 1) (list-pick (rest alos) (sub1 n))]))

Figure 75: Indexing into a list, simplified

Figure 75 displays this simplified version of list-pick. While it is far simpler than the original, it is important to understand that we designed the original in a systematic manner and that we were able to transform the first into the second with well-established algebraic laws. We can therefore trust this simple version. If we try to find the simple versions of functions directly, we sooner or later fail to take care of a case in our analysis, and we are guaranteed to produce flawed programs.

Exercise 327. Design the function replace-eol-with using the strategy of Processing Two Lists Simultaneously: Case 3. Start with the given tests. Simplify the resulting definition systematically.

Exercise 328. Simplify the function points-to from exercise 326.

26.5 Designing Functions that Consume Two Complex Inputs

The proper approach to designing functions of two (or more) complex arguments is to follow the general recipe. You must conduct a data analysis and define the relevant classes of data. If the use of parametric definitions such as List-of and short-hand examples such as '(1 b &) confuses you, expand them so that the constructors become explicit. Next you need a function signature and purpose. At this point, you can think ahead and decide which of the following three situations you are facing:
  1. If one of the parameters plays a dominant role, think of the other as an atomic piece of data as far as the function is concerned.

  2. In some cases the parameters range over the same class of values and must have the same size. Two lists must have the same length. Two Web pages must have the same length, and where one of them contains an embedded page, the other one does, too. If the two parameters have this equal status and the purpose suggests that they are processed in a synchronized manner, you choose one parameter, organize the function around it, and traverse the other in a parallel manner.

  3. If there is no obvious connection between the two parameters, you must analyze all possible cases as you pick examples and develop the template.

Once you decide that a function falls into the third category, develop a two-dimensional table to make sure that no case falls through the cracks. Let’s use a non-trivial pair of data definitions to explain this idea again:

(define-struct with [left info right])
; A LOD is one of:
;  '()
;  (cons Direction LOD)
; A TID is one of:
;  Symbol
;  (make-binary TID TID)
;  (make-with TID Symbol TID)

The left data definition is the usual list definition; the right one is a three-clause variant of TOS. It re-uses the binary structure type and the newly defined with, which is also a binary split but contains a symbol between the two trees.

Assuming the function consumes an LOD and a TID, the table that you should come up with has this shape:

(empty? l)

(cons? l)

(symbol? t)

(binary? t)

(with? t)

Along the horizontal direction we enumerate the conditions that recognize the subclasses for the first parameter, here LOD, and along the vertical direction we enumerate the conditions for the second parameter, TID.

The table guides the development of both the set of function examples and the function template. As explained, the examples must cover all possible cases, that is, there must be at least one example for each cell in the table. Similarly, the template must have one cond clause per cell; its condition combines the horizontal and the vertical condition in an and expression. Each cond clause, in turn, must contain all feasible selector expressions for both parameters. If one of the parameters is atomic, there is no need for a selector expression. Finally, you need to be aware of the feasible natural recursions. In general, all possible combinations of selector expressions (and optionally, atomic arguments) are candidates for a natural recursion. Because we can’t know which ones are necessary and which ones aren’t, we keep them in mind for the coding step.

In summary, the design of multi-parameter functions is just a variation on the old design-recipe theme. The key idea is to translate the data definitions into a table that shows all feasible and interesting combinations. The development of function examples and the template exploit the table as much as possible.

26.6 Exercises on Processing Two Complex Inputs

Exercise 329. Design merge. The function consumes two lists of numbers, sorted in ascending order. It produces a single sorted list of numbers that contains all the numbers on both inputs lists (and nothing else). A number occurs in the output as many times as it occurs on the two input lists together.

Exercise 330. Design the function drop. It consumes a list l and a natural number n. Its result is l with the first n items removed or just the empty list if l is too short.

Design the function take. It consumes a list l and a natural number n. Its result is the list of the first n items from l or all of l if it is too short.

Exercise 331. Hangman is a well-known guessing game. One player picks a word, the other play gets told how many letters the word contains. The second, guessing player picks a letter and asks the first player whether and where this letter occurs in the chosen word. If the guessing player uses more than some agreed-upon number of guesses, she loses; otherwise, she wins.

The goal of this exercise is to design reveal-list, the central function for this hangman game. This function consumes (a representation of) the word to be guessed, a word that represents how much/little the guessing player has uncovered, and the player’s current guess. Its result is another status word, revealing whether the guess occurred in the word and, if so, where.

Here is a suitable data definition:
(define LETTERS (explode "abcdefghijklmnopqrstuvwxyz"))
; A HMWord is [List-of [Maybe Letter]]
; A Letter is member? of LETTERS.
An underline in a HMWord represents an unknown letter. If a HMWord does not contain any underlines, it is a proper word.

Hence reveal consumes the following: a proper HMWord, a HMWord that may contain underlines, and a Letter. It replaces underlines with letters in the second argument if the corresponding letter in the first one is the same as the given guess.

Once you have designed the function, add it to the program below:
; String -> String
; runs a simplistic Hangman game where s is the word to be guessed,
; returns the guessed word
(define (main s)
  (local ((define the-word (explode s))
          (define the-guess (make-list (length the-word) "_"))
          ; HMWord -> Image
          ; renders the current word as an image
          (define (render-word w)
            (local ((define l
                      (map (lambda (l) (if (string? l) l "_")) w))
                    (define s (implode l)))
              (text s 33 "black")))
          ; HMWord KeyEvent -> HMWord
          ; updates the status if ke is a letter;
          ; otherwise, returns the current status
          (define (checked-reveal status ke)
              [(member? ke LETTERS) (reveal the-word status ke)]
              [else status])))
    ;  IN
      (big-bang the-guess
        [to-draw render-word]
        [on-key  checked-reveal]))))
Enjoy and refine as desired!

Exercise 332. In a factory, employees punch time cards as they arrive in the morning and leave in the evening. Electronic punch cards contain an employee number and record the number of hours worked per week. Employee records always contain the name of the employee, an employee number, and a pay rate.

Design wages*.v3. The function consumes a list of employee records and a list of punch-card records. It produces a list of wage records, which contain the name and weekly wage of an employee. The function signals an error if it cannot find an employee record for a punch-card record or vice versa.

You may assume that there is at most one punch-card record per employee number. An actual accounting program ensures that such assumptions hold.

Exercise 333. A linear combination is the sum of many linear terms, that is, products of variables and numbers. The latter are called coefficients in this context. Here are some examples:

In all three examples, the coefficient of x is 5, that of y is 17, and the one for z is 3.

If we are given values for variables, we can determine the value of a polynomial. For example, if x = 10, the value of is 50; if x = 10 and y = 1, the value of is 67; and if x = 10, y = 1, and z = 2, the value of is 73.

There are many different representations of linear combinations. We could, for example, represent them with functions. An alternative representation is a list of its coefficients. The above combinations would be represented as:
(list 5)
(list 5 17)
(list 5 17 3)
This choice of representation assumes a fixed order of variables.

Design value. The function consumes two equally long lists: a linear combination and a list of variable values. It produces the value of the combination for these values.

Exercise 334. Louise, Jane, Laura, Dana, and Mary decided to run a lottery that assigns one gift recipient to each of them. Since Jane is a computer programmer, they ask her to write a program that performs this lottery in an impartial manner. Of course, the program must not assign any of the sisters to herself.

Here is the core of Jane’s program:
; [List-of String] -> [List-of String]
; picks a random non-identity arrangement of names
(define (gift-pick names)
    (non-same names (arrangements names))))
; [List-of String] -> [List-of [List-of String]]
; returns all possible permutations of the given list of names
; see exercise 180
(define (arrangements names)
It consumes a list of names and randomly picks one of those permutations that do not agree with the original list at any place.

Your task is to design two auxiliary functions:
; [List-of X] -> X
; returns a random item from the list
; assume the list is not empty
(define (random-pick l)
  (first l))
; [List-of String] [List-of [List-of String]]
; ->
; [List-of [List-of String]]
; produces the list of those lists in ll that do not agree
; with names at any place
(define (non-same names ll)
Recall that random picks a random number; see exercise 89.

Exercise 335. Design the function DNAprefix. The function takes two arguments, both lists of 'a, 'c, 'g, and 't, symbols that occur in DNA descriptions. The first list is called a pattern, the second one a search string. The function returns #true if the pattern is a identical to the initial part of the search string. Otherwise the function returns #false.

Also design DNAdelta. This function is like DNAprefix but returns the first item in the search string beyond the pattern. if the lists are identical and there is no DNA letter beyond the pattern, the function signals an error. If the pattern does not match the beginning of the search string, the function returns #false. The function must not process either of the lists more than once.

Can DNAprefix or DNAdelta be simplified?

Exercise 336. Design sexp=?, a function that determines whether two S-exprs are equal. For convenience, we recall the data definition here though in a condensed form:
; An S-expr (S-expression) is one of:
;  Atom
;  [List-of S-expr]
; An Atom is one of:
;  Number
;  String
;  Symbol

Whenever you use check-expect, it uses a function like sexp=? to check whether the two arbitrary values are equal. If not, the check fails and check-expect reports it as such.

Exercise 337. Re-read exercise 289. Explain the reasoning behind our hint to think of the given expression as an atomic value at first.

27 Summary

This fourth part of the book is about the design of functions that process data whose description requires many intertwined definitions. These forms of data show up everywhere in the real world, from your computer’s local file system to the world wide web and geometric shapes used in animated movies. If you study this part of the book carefully, you know that the design recipe scales to these forms of data, too:
  1. When the description of program data calls for several mutually referential data definitions, the design recipe calls for the simultaneous development of templates, one per data definition. If a data definition A refers to a data definition B, then the template function-for-A refers to function-for-B in the exact same place and manner. Otherwise the design recipes work as before, function for function.

  2. When a function has to process two types of complex data, you need to distinguish three cases. First, the function may deal with one of the arguments as it were atomic. Second, the two arguments are expected to have the exact same structure, and the function traverses them in a completely parallel manner. Third, the function may have to deal with all possible combinations separately. In this case, you make a two-dimensional table that along one dimension enumerates all kinds of data from one data definition and along the other one deals with the second kind of data. Finally you use the table’s cells to formulate conditions and answers for the various cases.

    This part of the book deals with functions on two complex arguments. If you ever encounter one of those rare cases where a function receives three complex pieces of data, you know you need (to imagine) a three-dimensional table.

With this fourth part of the book, you have seen all forms of structural data that you are likely to encounter over the course of your career. The actual forms you will have to work with may differ in detail but the design recipe will always apply.