Expressiveness and Ambiguity: Learning to Program Can Be Unnecessarily Hard

One of the most important things to be able to do in any profession is to think as a professional. This is certainly true of Computer Science, because we have to spend so much time thinking as a Computer Scientist would think about how the machine will interpret our instructions. For those who don’t program, a brief quiz. What is the value of the next statement?

What is 3/4?

No doubt, you answered something like 0.75 or maybe 75% or possibly even “three quarters”? (And some of you would have said “but this statement has no intrinsic value” and my heartiest congratulations to you. Now go off and contemplate the Universe while the rest of us toil along on the material plane.) And, not being programmers, you would give me the same answer if I wrote:

What is 3.0/4.0?

Depending on the programming language we use, you can actually get two completely different answers to this apparently simple question. 3/4 is often interpreted by the computer to mean “What is the result if I carry out integer division, where I will only tell you how many times the denominator will go into the numerator as a whole number, for 3 and 4?” The answer will not be the expected 0.75, it will be 0, because 4 does not go into 3 – it’s too big. So, again depending on programming language, it is completely possible to ask the computer “is 3/4 equivalent to 3.0/4.0?” and get the answer ‘No’.

This is something that we have to highlight to students when we are teaching programming, because very few people use integer division when they divide one thing by another – they automatically start using decimal points. Now, in this case, the different behaviour of the ‘/’ is actually exceedingly well-defined and is not all ambiguous to the computer or to the seasoned programmer. It is, however, nowhere near as clear to the novice or casual observer.

I am currently reading Stephen Ramsay’s excellent “Reading Machines: Towards an Algorithmic Criticism” and it is taking me a very long time to read an 80 page book. Why? Because, to avoid ambiguity and to be as expressive and precise as possible, he has used a number of words and concepts with which I am unfamiliar or that I have not seen before. I am currently reading his book with a web browser and a dictionary because I do not have a background in literary criticism but, once I have the building blocks, I can understand his argument. In other words, I am having to learn a new language in order to read a book for that new language community. However, rather than being irked that “/” changes meaning depending on the company it keeps, I am happy to learn the new terms and concepts in the space that Ramsay describes, because it is adding to my ability to express key concepts, without introducing ambiguous shadings of language over things that I already know. Ramsay is not, for example, telling me that “book” no longer means “book” when you place it inside parentheses. (It is worth noting that Ramsay discusses the use of constraint as a creative enhancer, a la Oulipo, early on in the book and this is a theme for another post.)

The usual insult at this point is to trot out the accusation of jargon, which is as often a statement that “I can’t be bothered learning this” than it is a genuine complaint about impenetrable prose. In this case, the offender in my opinion is the person who decided to provide an invisible overloading of the “/” operator to mean both “division” and “integer division”, as they have required us to be aware of a change in meaning that is not accompanied by a change in syntax. While this isn’t usually a problem, spoken and written languages are full of these things after all, in the computing world it forces the programmer to remember that “/” doesn’t always mean “/” and then to get it the right way around. (A number of languages solve this problem by providing a distinct operator – this, however, then adds to linguistic complexity and rather than learning two meanings, you have to learn two ‘words’. Ah, no free lunch.) We have no tone or colour in mainstream programming languages, for a whole range of good computer grammar reasons, but the absence of the rising tone or rising eyebrow is sorely felt when we encounter something that means two different things. The net result is that we tend to use the same constructs to do the same thing because we have severe limitations upon our expressivity. That’s why there are boilerplate programmers, who can stitch together a solution from things they have already seen, and people who have learned how to be as expressive as possible, despite most of these restrictions. Regrettably, expressive and innovative code can often be unreadable by other people because of the gymnastics required to reach these heights of expressiveness, which is often at odds with what the language designers assumed someone might do.

We have spent a great deal of effort making computers better at handling abstract representations, things that stand in for other (real) things. I can use a name instead of a number and the computer will keep track of it for me. It’s important to note that writing int i=0; is infinitely preferable to typing “0000000000000000000000000000000000000000000000000000000000000000” into the correct memory location and then keeping that (rather large number) address written on a scrap of paper. Abstraction is one of the fundamental tools of modern programming, yet we greatly limit expressiveness in sometimes artificial ways to reduce ambiguity when, really, the ambiguity does seem a little artificial.

One of the nastiest potential ambiguities that shows up a lot is “what do we mean by ‘equals'”. As above, we already know that many languages would not tell you that “3/4 equals 3.0/4.0” because both mathematical operations would be executed and 0 is not the same as 0.75. However, the equivalence operator is often used to ask so many different questions: “Do these two things contain the same thing?”, “Are these two things considered to be the same according to the programmer?” and “Are these two things actually the same thing and stored in the same place in memory?”

Generally, however, to all of these questions, we return a simple “True” or “False”, which in reality reflects neither the truth nor the falsity of the situation. What we are asking, respectively, is “Are the contents of these the same?” to which the answer is “Same” or “Different”. To the second, we are asking if the programmer considers them to be the same, in which case the answer is really “Yes” or “No” because they could actually be different, yet not so different that the programmer needs to make a big deal about it. Finally, when we are asking if two references to an object actually point to the same thing, we are asking if they are in the same location or not.

There are many languages that use truth values, some of them do it far better than others, but unless we are speaking and writing in logical terms, the apparent precision of the True/False dichotomy is inherently deceptive and, once again, it is only as precise as it has been programmed to be and then interpreted, based on the knowledge of programmer and reader. (The programming language Haskell has an intrinsic ability to say that things are “Undefined” and to then continue working on the problem, which is an obvious, and welcome, exception here, yet this is not a widespread approach.) It is an inherent limitation on our ability to express what is really happening in the system when we artificially constrain ourselves in order to (apparently) reduce ambiguity. It seems to me that we have reduced programmatic ambiguity, but we have not necessarily actually addressed the real or philosophical ambiguity inherent in many of these programs.

More holiday musings on the “Python way” and why this is actually an unreasonable demand, rather than a positive feature, shortly.

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