This is a very complex problem on a very volatile dataset. It would be like asking a physicist to predict the outcome of an foot race by calculating a complete

This is a very complex problem on a very volatile dataset. It would be like asking a physicist to predict the outcome of an foot race by calculating a complete quantum description of all the runners. If that is your standard, then it is highly impractical, but not theoretically meaningless. However, "determining which of 2 pieces of code are better" is not such a problem. It is one that can be solved by heuristics and approximation, just the same as predicting the outcome of a race can. So while there is as of yet no perfect algorithm, I would strongly disagree that there is _no_ algorithm or that there is no way to compare different algorithms meaningfully. We have measurements, and just because they are not perfect doesn't mean they cannot be verifiably correct within a useful margin of error. We can reliably tell which two pieces of code are better. We cannot do it with extreme precision, and we cannot do it quickly enough to make predictions, but this does not mean that the concept of 'better code' is invalid or not understood in a productive sense. It certainly doesn't mean you can't make informal arguments from it.