Scrolling through this list, it’s clear that many are “correctness issues.”
I do not link this to argue that scipy bugs are more serious or more frequent. I don’t think that kind of statistical comparison is meaningful.
However, I think a motivated reasoner could write a very similar blog post to the OP, but about $arb_python_lib instead of $arb_julia_lib.
I suppose my position is closer to “only Kahan, Boyd and Higham write correct numerical algorithms” (a hyperbole, but illustrative of the difficulty level).
Explicit errors are not correctness issues. Some numerical instability issues in certain algorithms in certain corner cases might be considered as such though.
Regardless, overall, these are grossly of another complexity and seriousness than the base sum function being just wrong, or cultural issues among developers with not verifying inputs "for performance", or things of that nature. The scientific Python community has, in my experience, a much higher adherence to good standards than that.
I have found multiple "correctness bugs" of equal seriousness in Polars, and one of them is still open. that is not to throw shade at polars --- I love that package! but my point is that these things happen everywhere in software.
https://github.com/scipy/scipy/issues?q=is%3Aissue%20state%3...
Scrolling through this list, it’s clear that many are “correctness issues.”
I do not link this to argue that scipy bugs are more serious or more frequent. I don’t think that kind of statistical comparison is meaningful.
However, I think a motivated reasoner could write a very similar blog post to the OP, but about $arb_python_lib instead of $arb_julia_lib.
I suppose my position is closer to “only Kahan, Boyd and Higham write correct numerical algorithms” (a hyperbole, but illustrative of the difficulty level).