What is really curious is why, after being reinvented so many times, it is not more mainstream. I would love to talk to people who have tried using it in production and then decided it was a bad idea (if they exist).
[1]: https://www.boost.org/doc/libs/1_89_0/libs/numeric/interval/... [2]: https://arblib.org/
If you make some assumptions about your error (a popular one is to assume Gaussian distribution) then you can calculate the error of the result quite elegantly.
It's a nice excercise to write some custom C++ types that have (value, error) and automatically propagate them as you perform mathematical operations on them.
Unfortunately, in the real world only very few measurements have a gaussian error distribution, and the problem are systematic (non-random) errors, and reasoning about them is a lot harder.
So this means that automatically handling error propagation is in most cases pointless, since you need to manually analyze the situation anyway.