On a re-read you realise this isn’t really about Swift

It’s a bit early to have formed an opinion on a recently-announced programming language, but as the requisite number of people have asked what mine is (i.e. at least zero) I thought I’d type and see what happens.

Rules in programming tend to be bullshit. This is about one-third of a talk I’m giving later in the year, so I’ll leave that train of thought alone in case anyone’s going to be in the audience.

Anyway, knowing this, we can observe the exceptions to any rule people tend to throw at us about programming languages. For example: “Static types for good engineering, dynamic types for exploration“. Make sure your useing you’re type’s good, and notice how many engineering practices come from programmers who know dynamic languages.

We could add “you can’t do good tooling on dynamic languages”. O RLY.

Having thus realised that the rules are nonsense, and that some expert actually sat down and thought about what they wanted to get out of a language in the contexts in which they thought they’d use it. When I did that, I decided on something that goes in entirely the opposite direction to Swift. Starting with ObjC, I’m more likely to end up with Self than with Swift (but then I’ve already told you that I think Sun Microsystems did awesome shit, so that may be no surprise). It probably just means that I’m not an expert, though.

If you’re going to use types, may as well go all-in on using tuples. You’ll probably see plenty of definitions of a tuple, so let me confuse things by adding another:

A tuple is an element drawn from the cartesian product of its member types.

What this means is that unlike boring old-fashioned ordered collections, there’s a known number of things in a tuple and each is of a known type. This is useful for the vexing question of signalling errors, as you can just return something like a (Result?, Error?) like an industrialised society ought to be doing. It also means that almost every situation in which a method’s interface accepts or returns a collection can be replaced with a situation in which it accepts a known number of things of known type.

But anyway, for the most part our programming languages allow us to accidentally introduce problems that we shouldn’t need to solve, but the biggest problems we actually have to solve lie elsewhere.

It’s just like English

Fans of the RSpec tool for writing tests will be familiar with its English-like(fn1) syntax for describing tests, which looks like this.

describe StrawMan do
  context "when interpreting a test in RSpec" do
    it "is written in plain English" do
      expect(spec).to eq(legible_text)
    end
  end
end

That’s almost completely distinguishable from conversational English. Perhaps programmers just have a different idea of what English looks like than many typical speakers of English. I posit this conclusion because the gulf between “English-like” and “English” is not new. You can almost see attempts at real constructs in the English language being bashed into place in the syntax for BASIC:

“For every number between 1 and 10, do this with the number being named ‘I’…that’s everything, so move on to the next value for I now.”

FOR I = 1 TO 10 : … : NEXT I

And the the the Apple-recommended Definitive Guide to the AppleScript has this the the to say the about the “English-likeness” monster:(fn2)

Personally, though, I’m not fond of AppleScript’s English-likeness. For one thing, I feel it is misleading. It gives one the sense that one just knows AppleScript because one knows English; but that is not so. It also gives one the sense that AppleScript is highly flexible and accepting of commands expressed just however one cares to phrase them; and that is really not so.

Reviewing, then, we have a collection of tools that claim some similarity with English, but then fall down on every comparison except “uses some sequences of characters that have also been used in English”. What went wrong? Indeed, did anything go wrong?

Programming’s close analogue in natural language is the Arabic wish. Computers are much like the djinn in that you tell them what should happen and instead they make exactly the thing you asked for. You waste two attempts to converse with them on asking reasonable questions that they wilfully misinterpret, then spend forever agonising over your third and final attempt. With a djinni, it’s your final attempt because you only got three wishes. With a computer, you’re allowed to try as often as you like but by the third time you’re realising how much more appealing a career in assassinating mythical preternatural wish-givers is looking but you don’t want to take that kind of risk. Both djinn and computers are like that person who’s had a restraining order ever since they decided to take “pick me up at 8” more literally than was truly warranted.

So the role of the programmer is like a kind of djinn-lawyer, translating all of the nuance and creative ambiguity of conversational language into the sort of precise, single-meaning prose that even the most belligerent of readers cannot deliberately misinterpret. And that bit of programming has not materially changed in decades. We’ve gone from “do exactly this”, through “do exactly this but you choose how you use the register file to do it” and “do exactly this but you choose how you use the main memory to do it”.

Getting computers to act like participants in a conversation is possible, but either a bit of a gimmick or limited in application. If you really wanted to build the Knowledge Navigator you’d need to fix this problem (along with the attendant acoustic engineering problems).

That is when we’ll actually be able to claim success at improvement through abstraction. Not when we can give specialist djinn-linguists more abstractions, but when we can give computers enough abstractions that you no longer need to be a translator to make a computer do anything.

(fn1) Why “English-like” and not “verbal language-like”? One might chalk it up to neocolonialism and American industry deciding that English was Good Enough For Everybody. Indeed, as Matz notes, many Japanese people cannot speak English well and that adds a barrier to learning programming languages that are sort-of in English.

(fn2) Please don’t write in about all of the spurious occurrences of “the” in the last (non-quote) sentence. For those of us who have used AppleScript, they can be our little in-joke.

How much programming language is enough?

Many programmers have opinions on programming languages. Maybe, if I present an opinion on programming languages, I can pass off as a programmer.

An old debate in psychology and anthropology is that of nature vs nurture, the discussion over which characteristics of humans and their personalities are innate and which are learned or otherwise transferred.

We can imagine two extremists in this debate turning their attention to programming languages. On the one hand, you might imagine that if the ability to write a computer program is somehow innate, then there is a way of expressing programming concepts that is closely attuned to that innate representation. Find this expression, and everyone will be able to program as fast as they can think. Although there’ll still be arguments over bracket placement, and Dijkstra will still tell you it’s rubbish.

On the other hand, you might imagine that the mind is a blank slate, onto which can be writ any one (or more?) of diverse patterns. Then the way in which you will best express a computer program is dependent on all of your experiences and interactions, with the idea of a “best” way therefore being highly situated.

We will leave this debate behind. It seems that programming shares some brain with learning other languages, and when it comes to deciding whether language is innate or learned we’re still on shaky ground. It seems unlikely on ethical grounds that Nim Chimpsky will ever be joined by Charles Babboonage, anyway.

So, having decided that there’s still an open question, there must exist somewhere into which I can insert my uninvited opinion. I had recently been thinking that a lot of the ceremony and complexity surrounding much of modern programming has little to do with it being difficult to represent a problem to a computer, and everything to do with there being unnecessary baggage in the tools and languages themselves. That is to say that contrary to Fred Brooks’s opinion, we are overwhelmed with Incidental Complexity in our art. That the mark of expertise in programming is being able to put up with all the nonsense programming makes you do.

From this premise, it seems clear that less complex programming languages are desirable. I therefore look admirably at tools like Self, io and Scheme, which all strive for a minimum number of distinct parts.

However, Clemens Szyperski from Microsoft puts forward a different argument in this talk. He works on the most successful development environment. In the talk, Szyperski suggests that experienced programmers make use of, and seek out, more features in a programming language to express ideas concisely, using different features for different tasks. Beginners, on the other hand, benefit from simpler languages where there is less to impede progress. So, what now? Does the “less is more” principle only apply to novice programmers?

Maybe the experienced programmers Szyperski identified are not experts. There’s an idea that many programmers are expert beginners, that would seem to fit Szyperski’s model. The beginner is characterised by a microscopic, non-holistic view of their work. They are able to memorise and apply heuristic rules that help them to make progress.

The expert beginner is someone who has simply learned more rules. To the expert beginner, there is a greater number of heuristics to choose from. You can imagine that if each rule is associated with a different piece of programming language grammar, then it’d be easier to remember the (supposed) causality behind “this situation calls for that language feature”.

That leaves us with some interesting open questions. What would a programming tool suitable for experts (or the proficient) look like? Do we have any? Alan Kay is fond of saying that we’re stuck with novice-friendly user experiences, that don’t permit learning or acquiring expertise:

There is the desire of a consumer society to have no learning curves. This tends to result in very dumbed-down products that are easy to get started on, but are generally worthless and/or debilitating. We can contrast this with technologies that do have learning curves, but pay off well and allow users to become experts (for example, musical instruments, writing, bicycles, etc. and to a lesser extent automobiles).

Perhaps, while you could never argue that common programming languages don’t have learning curves, they are still “generally worthless and/or debilitating”. Perhaps it’s true that expertise at programming means expertise at jumping through the hoops presented by the programming language, not expertise at telling a computer how to solve a problem in the real world.