__Programming Considered as a Human Activity.__

by

Edsger W. Dijkstra

__1. Introduction.__

By way of introduction I should like to start this talk with a story and a quotation.

The story is about the physicist Ludwig Boltzmann, who was willing to reach his goals by lengthy computations. Once somebody complained about the ugliness of his methods, upon which complaint Boltzmann defended his way of working by stating that "elegance was the concern of tailors and shoemakers", implying that he refused to be troubled by it.

In contrast I should like to quote another famous nineteenth century scientist, George Boole. In the middle of his book "An Investigation of the Laws of Thought" in a chapter titled "Of the Conditions of a Perfect Method." he writes: "I do not here speak of that perfection only which consists in power, but of that also which is founded in the conception of what is fit and beautiful. It is probable that a careful analysis of this question would conduct us to some such conclusion as the following, viz., that a perfect method should not only be an efficient one, as respects the accomplishment of the objects for which it is designed, but should in all its parts and processes manifest a certain unity and harmony". A difference in attitude one can hardly fail to notice.

Our unconscious association of elegance with luxury may be one of the origins of the not unusual tacit assumption that it costs to be elegant. To show that it also pays to be elegant is one of my prime purposes. It will give us a clearer understanding of the true nature of the quality of programs and the way in which they are expressed, viz. the programming language. From this insight we shall try to derive some clues as to which programming language features are most desirable. Finally we hope to convince you that the different aims are less conflicting with one another than they might thought to be at first sight.

__2. On the Quality of the results.__

Even under the assumption of flawlessly working machines we should ask ourselves the questions: "When an automatic computer produces results, why do we trust them, if we do so?" and after that; "What measures can we take to increase our confidence that the results produced are indeed the results intended?"

How urgent the first question is might be illustrated by a simple, be it somewhat simplified example. Suppose that a mathematician interested in number theory has at his disposal a machine with a program to factorize numbers. This process may end in one of two ways: either it gives a factorization of the number given or it answers that the number given is prime. Suppose now that our mathematician wishes to subject to this process a, say, 20 decimal number, while he has strong reasons to suppose that it is a prime number. If the machine confirms this expectation, he will be happy; if it finds a factorization, the mathematician may be disappointed because his intuition has fooled him again, but, when doubtful, he can take a desk machine and can multiply the factors produced in order to check whether the product reproduces the original number. The situation is drastically changed, however, if he expects the number given to be non-prime: if the machine now produces factors he finds his expectations confirmed and moreover he can check the result by multiplying. If, however, the machine comes back with the answer that the number given is, contrary to his expectations and warmest wishes, alas a prime number, why on earth should he believe this?

Our example shows that even in completely discrete problems the computation of a result is not a well-defined job, well-defined in the sense that one can say: "I have done it." without paying attention the convincing power of the result, viz. to its "quality".

The programmer's situation is closely analogous to that of the pure mathematician, who develops a theory and proves results. For a long time pure mathematicians have thought —and some of them still think— that a theorem can be proved completely, that the question whether a supposed proof for a theorem is sufficient or not, admits an absolute answer "yes" or "no". But this is an illusion, for as soon as one thinks that one has proved something, one has still the duty to prove that the first proof was flawless, and so on, ad infinitum! One can never guarantee that a proof is correct, the best one can say, is: "I have not discovered any mistakes". We sometimes flatter ourselves with the idea of giving watertight proofs, but in fact we do nothing but make the correctness of our conclusions plausible. So extremely plausible, that the analogy may serve as a great source of inspiration.

In spite of all its deficiencies, mathematical reasoning presents an outstanding model of how to grasp extremely complicated structures with a brain of limited capacity. And it seems worthwhile to investigate to what extent these proven methods can be transplanted to the art of computer usage. In the design of programming languages one can let oneself be guided primarily by considering "what the machine can do". Considering, however, that the programming language is the bridge between the user and the machine —that it can, in fact, be regarded as his tool— it seems just as important to take into consideration "what Man can think". It is in this vein that we shall continue our investigations.

__3. On the structure of convincing programs.__

The technique of mastering complexity is known since ancient times: "Divide et impera" ("Divide and rule"). The analogy between proof construction and program construction is, again, striking. In both cases the available starting points are given (axioms and existing theory versus primitives and available library programs), in both cases the goal is given (the theorem to be proven versus the desired performance), in both cases the complexity is tackled by division into parts (lemmas versus subprograms and procedures).

I assume the programmer's genius matched to the difficulty of his problem and assume that he has arrived at a suitable subdivision of the task. He then proceeds in the usual manner in the following stages:

- he makes the complete specifications of the individual parts

- he satisfies himself that the total problem is solved provided he had at his disposal program parts meeting the various specifications

- he constructs the individual parts, satisfying the specifications, but independent of one another and the further context in which they will be used.

Obviously, the construction of such an individual part may again be a task of such complexity, that inside this part job, a further subdivision is required.

Some people might think the dissection technique just sketched a rather indirect and tortuous way of reaching ones goals. My own feelings are perhaps best described by saying that I am perfectly aware that there is no Royal Road to Mathematics, in other words, that I have only a very small head and must live with it. I, therefore, see the dissection technique as one of the rather basic patterns of human understanding and think it worthwhile to try to create circumstances in which it can be most fruitfully applied.

The assumption that the programmer had made a suitable subdivision finds its reflection in the possibility to perform the first two stages: the specification of the parts and the verification that they together do the job. Here elegance, accuracy, clarity and a thorough understanding of the problem at hand are prerequisite. But the whole dissection techniques relies on something less outspoken, viz. on what I should like to call "The principle of non-interference". In stage II it is assumed that the correct working of the whole can be established by taking, of the parts, into account their exterior specification only, and not the particulars of their interior construction. In stage III the principle of non-interference pops up again: here it is assumed that the individual parts can be conceived and constructed independently from one another.

This is perhaps the moment to mention that, provided I interpret the signs of current attitudes towards the problems of language definition correctly, in some more formalistic approaches the soundness of the dissection technique is made subject to doubt. Their promoters argue as follows: whenever you give of a mechanism such a two stage definition, first what it should do, viz. its specifications, and secondly how it works, you have, at best, said twice the same thing, but in all probability you have contradicted yourself. And statistically speaking, I am sorry to say, this last remark is a strong point. The only clean way towards language definition, they argue, is by just defining the mechanisms, because what they then will do will follow from this. My question: "How does this follow?" is wisely left unanswered and I am afraid that their neglection of the subtle, but sometimes formidable difference between the concepts "defined" and "known" will make their efforts an intellectual exercise leading into another blind alley.

After this excursion we return to programming itself. Everybody familiar with ALGOL 60 will agree that its procedure concept satisfies to a fair degree our requirements of non-interference, both in its static properties (e.g. in the freedom in the choice of local identifiers) as in its dynamic properties (e.g. the possibility to call a procedure, directly or indirectly, from within itself).

Another striking example of increase of clarity through non-interference, guaranteed by structure, is presented by all programming languages in which algebraic expressions are allowed. Evaluation of such expressions with a sequential machine having an arithmetic unit of limited complexity will imply the use of temporary store for the intermediate results. Their anonymity in the source language guarantees the impossibility that one of them will inadvertently be destroyed before it is used, as would have been possible if the computational process were described in a von Neumann type machine code.

__4. A comparison of some alternatives.__

A broad comparison between a von Neumann type machine code —well known for its lack of clarity— and different types of algorithmic languages may be not out of order.

In all cases the execution of a program consists of a repeated confrontation of two information streams, the one (say "the program") constant in time, the other (say "the data") varying. For many years it has been thought one of the essential virtues of the von Neumann type code that a program could modify its own instructions. In the mean time we have discovered that exactly this facility is to a great extent responsible for the lack of clarity in machine code programs. Simultaneously its indispensability has been questioned: all algebraic compilers I know produce an object program that remains constant during its entire execution phase.

This observation brings us to consider the status of the variable information. Let us first confine our attention to programming languages without assignment statements and without **goto** statements. Provided that the spectrum of admissible function values is sufficiently broad and the concept of the conditional expression is among the available primitives, one can write the output of every program as the value of a big (recursive) function. For a sequential machine this can be translated into a constant object program, in which at run time a stack is used to keep track of the current hierarchy of calls and the values of the actual parameters supplied at these calls.

Despite its elegance a serious objection can be made against such a programming language. Here the information in the stack can be viewed as objects with nested life times and with a constant value during their entire life time. Nowhere (except in the implicit increase of the order counter which embodies the progress of time) the value of an already existing named object is replaced by another value. As a result the only way to store a newly formed result is by putting it on top of the stack; we have no way of expressing that an earlier value becomes now obsolete and the latter's life time will be prolonged, although void of interest. Summing up: it is elegant but inadequate. A second objection —which is probably a direct consequence of the first one— is that such programs become after a certain, quickly attained degree of nesting, terribly hard to read.

The usual remedy is the combined introduction of the **goto** statement and the assignment statement. The **goto** statement enables us with a backward jump to repeat a piece of program, while the assignment statement can create the necessary difference in status between the successive repetitions.

But I have reasons to ask, whether the **goto** statement as a remedy is not worse than the defect it aimed to cure. For instance, two programming department managers from different countries and different backgrounds —the one mainly scientific, the other mainly commercial— have communicated to me, independently of each other and on their own initiative, their observation that the quality of their programmers was inversely proportional the density of **goto** statements in their programs. This has been an incentive to try to do away with the **goto** statement.

The idea is, that what we know as "transfer of control", i.e. replacement of the order counter value, is an operation usually implied as part of more powerful notations: I mention the transition to the next statement, the procedure call and return, the conditional clauses and the **for** statement; and it is the question whether the programmer is not rather led astray by giving him separate control over it.

I have done various programming experiments and compared the ALGOL text with the text I got in modified versions of ALGOL 60 in which the **goto** statement was abolished and the **for** statement —being pompous and over-elaborate— being replaced by a primitive repetition clause. The latter versions were more difficult to make: we are so familiar with the jump order that it requires some effort to forget it! In all cases tried, however, the program without **goto** statements turned out to be shorter and more lucid.

The origin in the increase in clarity is quite understandable. As is well known there exists no algorithm to decide whether a given program ends or not. In other words: each programmer who wants to produce a flawless program must at least convince himself by inspection that his program will indeed terminate. In a program, in which unrestricted use of the **goto** statement has been made this analysis may be very hard on account of the great variety of ways in which the program may fail to stop. After the abolishment of the **goto** statement there are only two ways in which a program may fail to stop: either by infinite recursion —i.e. through the procedure mechanism— or by the repetition clause. This simplifies the inspection greatly.

The notion of repetition, so fundamental in programming, has a further consequence. It is not unusual that inside a sequence of statements to be repeated one or more subexpressions occur, which do not change their value during the repetition. If such a sequence is to be repeated many times, it would be a regrettable waste of time if the machine had to recompute these same values over and over again. One way out of this is to delegate to the now optimizing translator the discovery of such constant subexpressions in order that it can take the computation of their values outside the loop. Without an optimizing translator the obvious solution is to invite the programmer to be somewhat more explicit and he can do so by introducing as many additional variables as there are constant subexpressions within the repetition and by assigning the values to them before entering the repetition. I should like to stress that both ways of writing the program are equally misleading. In the first case the translator is faced with the unnecessary puzzle to discover the constancy, in the second case we have introduced a variable, the only function of which is to denote a constant value. This last observation shows the way out of the difficulty: besides variables the programmer would be served by "local constants", i.e. identifiable quantities with a finite lifetime, during which they will have a constant value, that has been defined at the moment of introduction of the quantity. Such quantities are not new: the formal parameters of procedures already display this property. The above is a plea to recognize that the concept of the "local constant" has its own right of existence. If I am well informed, this has already been recognized in CPL, the programming language designed in a joint effort around the Mathematical Laboratory of the University of Cambridge, England.

__5. The double gain of clarity.__

I have discussed at length that the convincing power of the results is greatly dependent on the clarity of the program, on the degree in which it reflects the structure of the process to be performed. For those who feel themselves mostly concerned with efficiency as measured in the cruder units of storage and machine time, I should like to point out that increase of efficiency always comes down to exploitation of structure and for them I should like to stress that all structural properties mentioned can be used to increase the efficiency of an implementation. I shall review them briefly.

The lifetime relation satisfied by the local quantities of procedures allows us to allocate them in a stack, thus making very efficient use of available store; the anonymity of the intermediate results enables us to minimize storage references dynamically with the aid of an automatically controlled set of push down accumulators; the constancy of program text under execution is of great help in machines with different storage levels and reduces the complexity of advanced control considerably; the repetition clause eases the dynamic detection of endless looping and finally, the local constant is a successful candidate for a write-slow-read-fast store, when available.

Let me conclude. When I became acquainted with the notion of algorithmic languages I never challenged the then prevailing opinion that the problems of language design and implementation were mostly a question of compromises: every new convenience for the user had to be paid for by the implementation, either in the form of increased trouble during translation, or during execution or during both. Well, we are most certainly not living in Heaven and I am not going to deny the possibility of a conflict between convenience and efficiency, but I do now protest when this conflict is presented as a complete summing up of the situation. I am of the opinion that is worthwhile to investigate to what extent the needs of Man and Machine go hand in hand and to see what techniques we can devise for the benefit of all of us. I trust that this investigation will bear fruits and if this talk made some of you share this fervent hope, it has achieved its aim.

Prof. Dr. Edsger W. Dijkstra

Department of Mathematics

Technological University

P.O.Box 513

__EINDHOVEN__

The Netherlands

transcribed by Nolan Egly

revised