Empirically estimating big-oh time efficiency

Question

Background

I\'d like to estimate the big-oh performance of some methods in a library through benchmarks. I don\'t need precision -- it suffices to show that someth

Answer 1

I'm not sure I get 100% what you want. But I understand that you test your own code, so you can modify it, e.g. inject observing statements. Otherwise you could use some form of aspect weaving?

How about adding resetable counters to your data structures and then increase them each time a particular sub-function is invoked? You could make those counting @elidable so they will be gone in the deployed library.

Then for a given method, say delete(x), you would test that with all sorts of automatically generated data sets, trying to give them some skew, etc., and gather the counts. While as Igor points out you cannot verify that the data structure won't ever violate a big-O bound, you will at least be able to assert that in the actual experiment a given limit count is never exceeded (e.g. going down a node in a tree is never done more than 4 * log(n) times) -- so you can detect some mistakes.

Of course, you would need certain assumptions, e.g. that calling a method is O(1) in your computer model.