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?
I know far too little about compilers & interpreters to have anything to say about performance so I’ll leave that subject to wiser programmers.
What I can say about the usage itself of dynamically vs statically typed languages is that I struggle with assessments that attempt to quantify the differences between the two paradigms. I’ve come to consider programming has a craft, and as such the qualitative properties of the tools, and especially the languages, matter significantly.
I’ve been switching back and forth between dynamic and static languages lately. Although dynamic languages do feel more straight to the point, static languages are easier to navigate through. All that typing information can be harnessed by intellisense and empower the non-linear reading needed to understand a program. That’s valuable for the whole life cycle of the software, not just the time to reach initial release. It’s kind of a rigid vs fluid dichotomy.
There are also in-between approaches. Python for example has optional typing (for type checks with mypy), whereas Lisp/SBCL allows type hints for performance. Racket and Clojure allow to add types as pre-conditions (Typed Racket and Clojure Soec).
And many modern languages like Scala or Rust mostly need types in the function signature - the rest of the time, types are usually inferred. Even languages which were rigorously typed in the past, like C++, have the auto keyword added which activates type inference.
Oh I love it when the language has advanced type inference! I have fond memories of tinkering with Haxe.
Hindley-milner type inference for the win!
It’s hard to implement, but the result is a statically typed language, mostly without type annotations.
Rust and OCaml use this.