It's worth noting that we initially migrated ~15kloc of numpy data/numeric pipelines to Julia, and mostly found the same values, with the exception of something that turned out to be a bug in a Python library.
I would say that the biggest Julia annoyance we've run into has to do with the way the Expr type is implemented, particularly the fact that it's mutable, which makes some of the metaprogramming we want to do substantially harder. But that's not a bug per se, just a design choice I don't like.
* usage of `OffsetArrays.jl` (really, just avoiding this package fixes most of the issues)
* hideously cursed syntax that should only pass a code review if your name is Lovecraft, but for some reason the parser allows it
if you don't do either of those things (which at least personally speaking, I don't) then I don't think the rate of "correctness issues" is any higher or lower than I experience in other ecosystems. In fact, it's probably lower
Don't forget that other languages are not immune... I love the `polars` library as well but in my two years of using it I've encountered organically two separate "correctness" bugs. It's just par for the course for any big code surface
In addition, I believe that abstract types are not that horrible since in industry we use OOP any way (https://github.com/Suzhou-Tongyuan/ObjectOriented.jl). I coworked with the author of this package several years ago. Currently, he is developping a different branch of Julia compiler. Since OOP makes eveything easier to design (from linter to static compiler), and programmers prefer OOP over abstract types, I personally don't think they will cause huge problems.