Programmatically obtaining Big-O efficiency of code

Question

I wonder whether there is any automatic way of determining (at least roughly) the Big-O time complexity of a given function?

If I graphed an O(n) function vs. an O(n

Answer 1

A short answer is that it's impossible because constants matter.

For instance, I might write a function that runs in O((n^3/k) + n^2). This simplifies to O(n^3) because as n approaches infinity, the n^3 term will dominate the function, irrespective of the constant k.

However, if k is very large in the above example function, the function will appear to run in almost exactly n^2 until some crossover point, at which the n^3 term will begin to dominate. Because the constant k will be unknown to any profiling tool, it will be impossible to know just how large a dataset to test the target function with. If k can be arbitrarily large, you cannot craft test data to determine the big-oh running time.