Cheers That looks really interesting - I saw that you use an underlying statistical test for the nul hypothesis. That is a cool way to approach it.
Best, Kasper > On 12 Aug 2021, at 19.44, Evan Donahue <emdon...@gmail.com> wrote: > > Hello, > > I've just released a utility I've been using for the past few years to help > optimize Pharo code. It's a library that makes it easy to check whether an > optimization has improved performance by wrapping the optimization and the > old code in an if block and executing the optimized and unoptimized paths > repeatedly under the same runtime conditions to determine which is faster. > > Basic usage is to wrap changes to the code in if blocks like so: > > ABBench a: [ ...existing code...] b: [...optimized code...] > > And then run the A/B test by printing: > > ABBench bench: [...some main method...] > > I have found it to make performance testing simple and quick enough that I > actually do it. Perhaps someone else will find it useful. > > Install with: > > Metacello new > githubUser: 'emdonahue' project: 'ABBench' commitish: 'master' path: ''; > baseline: 'ABBench'; > load. > > More details can be found here: > > https://github.com/emdonahue/ABBench <https://github.com/emdonahue/ABBench> > > Cheers, > Evan
