[rbehrends]

> But this is not the only cost that matters, indeed might not even be a cost.

I'm not saying otherwise. My point is that there's no objective way to proclaim one better than the other. This depends on application domain, economic constraints, engineering constraints, what you're doing, and so forth.

Writing Ada software that controls a pacemaker has totally different requirements than exploratory programming in Jupyter that mostly deals with integers, floats, and arrays and matrices thereof, for example.

[adrianratnapala]

> I'm not saying otherwise. My point is that there's no objective way to proclaim one better than the other.

Very true. But any analysis that emphasizes writing code over maintaining it will systematically bias itself in favor of dynamic typing.

Interestingly I have had the converse debate with some of my colleagues, who have learned to hate Python because they keep having to debug existing systems. I try to tell them that it is an excellent language for the kind of one-off data-analysis that I did when I was a scientist.

They don't believe me, because here among software engineers, seemingly innocent 400 line scripts keep growing into giant, decade old, 100kloc typeless monstrosities.

[rbehrends]

> Very true. But any analysis that emphasizes writing code over maintaining it will systematically bias itself in favor of dynamic typing.

This is not what the studies do. There is no emphasis on anything. They look at how people do on a number of different tasks with different typing options and report the results.

Also, it's not just dynamic vs. static typing. Gradual and soft typing is also of interest, because it allows you to turn dynamically typed code into statically typed code without a complete rewrite.