In 2016 yes. But this has been very much addressed. At this point, not only is Julia well-tested, but it's so well-tested that it has to carry around its own patched LLVM and its own math library in order for its full tests to pass, because for example it requires things like sin to be correct to 1ulp which isn't actually true in a lot of implementations! Then when you go to packages, there's a huge culture of testing. Compare OrdinaryDiffEq's (just the ODE part of DifferentialEquations.jl) test suite:
which does convergence tests on every algorithm, along with regression tests on every interpolation and analytical value unit tests on each feature, etc. Meanwhile, scipy integrate has some of its own algorithms, but only a few regression tests, and most tests just check that things run: https://github.com/scipy/scipy/tree/master/scipy/integrate/t... . Same with other libraries like torchdiffeq: https://github.com/rtqichen/torchdiffeq/tree/master/tests . So Julia's testing culture is now strict enough that things that are commonly accepted in other programming languages would be a rejected PR due to lack of testing! And for good reason: these tests catch what would be "performance regressions" (i.e. regressions so the algorithm doesn't hit its theoretical properties) all the time!
https://travis-ci.org/JuliaDiffEq/OrdinaryDiffEq.jl
which does convergence tests on every algorithm, along with regression tests on every interpolation and analytical value unit tests on each feature, etc. Meanwhile, scipy integrate has some of its own algorithms, but only a few regression tests, and most tests just check that things run: https://github.com/scipy/scipy/tree/master/scipy/integrate/t... . Same with other libraries like torchdiffeq: https://github.com/rtqichen/torchdiffeq/tree/master/tests . So Julia's testing culture is now strict enough that things that are commonly accepted in other programming languages would be a rejected PR due to lack of testing! And for good reason: these tests catch what would be "performance regressions" (i.e. regressions so the algorithm doesn't hit its theoretical properties) all the time!