Summary by Dan Luu on the question about whether for statically typed languages, objective advantages (like having measurably fewer bugs, or solving problems in measurably less time) can be shown.
If I think about this, authors of statically typed languages in general at their beginning might not even have claimed that they have such advantages. Originally, the objective advantage was that for computers like a PDP11 - which had initially only 4 K of memory and a 16-bit adress space - was that something like C or Pascal compilers could run on them at all, and even later C programs were much faster than Lisp programs of that time. At that time, it was also considered an attribute of the programming language whether code was compiled to machine instructions or interpreted.
Todays, with JIT compilation like in Java and the best implementation of Common Lisp like SBCL being at a stone's throw of the performance of Java programs, this distinction is not so much relevant any more.
Further, opinions might have been biased by comparing C to memory-safe languages, in other words, when there were perceived actual productivity gains, the causes might have been confused.
The thing which seems more or less firm ground is that the less lines of code you need to write to cover a requirement, the fewer bugs it will have. So more concise/expressive languages do have an advantage.
There are people which have looked at all the program samples in the above linked benchmark game and have compared run-time performamce and size of the source code. This leads to interesting and sometimes really unintuitive insights - there are in fact large differences between code sizes for the same task between programming languages, and a couple of different languages like Scala, JavaScript, Racket(PLT Scheme) and Lua come out quite well for the ratio of size and performance.
But given all this, how can one assess productivity, or the time to get from definition of a task to a working program, at all?
And the same kind of questions arise for testing. Most people would agree nowadays that automated tests are worth their effort, that they improve quality / shorten the time to get something working / lead to fewer bugs. (A modern version of the Joel Test might have automated testing included, but, spoiler: >!Joel's list does not contain it.!<)
Testing in small units also interacts positively with a "pure", side-effect-free, or 'functional' programming style... with the caveat perhaps that this style might push complex I/O functions of a program to its periphery.
It feels more solid to have a complex program covered by tests, yes, but how can this be confirmed in an objective way? And if it can, for which kind of software is this valid? Are the same methodologies adequate for web programming as for industrial embedded devices or a text editor?
Not sure I agree. I do think you're right - it's hard to prove these things because it's fundamentally hard to prove things involving people, and also because most of the advantages of static types are irrelevant for tiny programs which is what many studies use.
But I don't think that means you can't use your judgement about it and come to a conclusion. Especially with languages like Python and Typescript that allow an
anycop-out, it's hard to see how anyone could really conclude that they aren't better.Here's another example I came across recently: should bitwise
&have lower precedence than==like it does in C? Experience has told us that the answer is definitely no, and virtually every modern language puts them the other way around. Is it an open question? No. Did anyone prove this? Also no.Well, you can conclude anything using your reasoning, but that does give the high degree of certainty that is sought after in the studies reviewed in the article.
Again, I'm not saying that I don't believe static type checkers are beneficial, I'm just saying we cannot say that for sure.
It's like saying seat belts improve crash fatality rates. The claim seems plausible and you can be a paramedic to see the effects of seat belts first-hand and form a strong opinion on the matter. But still, we need studies to inspect the impact under scrutiny. We need studies in controlled environments to control for things like driver speed and exact crash scenarios, we need open studies to confirm what we expect really is happening on a larger scale.
Same holds for static type checkers. We are paramedics, who see that we should all be wearing seat belts of type annotations. But it might be that we are some subset of programmers dealing with problems that benefit from static type checking much more than average programmer. Or there might be some other hidden variable, that we cannot see, because we only see results of code we personally write.
No I disagree. There are some things that it's really infeasible to use the scientific method for. You simply can't do an experiment for everything.
A good example is UBI. You can't do a proper experiment for it because that would involve finding two similar countries and making one use UBI for at least 100 years. Totally impossible.
But that doesn't mean you just give up and say "well then we can't know anything at all about it".
Or closer to programming: are comments a good idea, or should programming languages not support comments? Pretty obvious answer right? Where's the scientific study?
Was default case fallthrough a mistake? Obviously yes. Did anyone ever do a study on it? No.
You don't always need a scientific study to know things to a reasonable certainty and often you can't do that.
That said I did see one really good study that shows Typescript catches about 15% of JavaScript bugs. So we don't have nothing.
Just because we cannot prove something, doesn't mean that we can treat strong claims the same way as proven hypnosis. If we cannot prove that UBI is overall beneficial, we just cannot believe it with the same certainty that we would if we had a bunch of studys on our side.
Look, I'm not saying that we have nothing - I'm just saying that what we have are educated guesses, not proven facts. Maybe "open question" was too strong of a term.
Yeah I agree. Scientific studies are usually a higher standard of proof. (Though they can also be wrong - remember "power poses"?) So it's more like we're 80% sure instead of 90%.