Hm. I think my main issue there is not the speed, but rather seeing the whole picture at once. You mentioned you use this pattern to test regular expressions; say you modify the regexp in question with some new feature requirement, and now the very first of a dozen test inputs fails. You fix it, but then each one of the following keeps failing, and you can only find an elegant solution that works for all of them after seeing all the failures, having ran the test and modified the code a dozen times. Wouldn't it be nicer to see all fails right away and be able to find a solution to all of them, instead of fixing the inputs one-by-one?
In my experience, from doing some TDD Katas[0] and timing myself, I found coding slower and more difficult when focusing on multiple examples at once.
I usually even comment out all the failing tests but the first one, after translating a bunch of specifications into tests, so I see the "green" when an example starts working.
Maybe it would be easier to grok multiple regex examples than algorithmic ones, but at least for myself, I am skeptical, and I prefer taking them one at a time.
In my own experience, this has often been a good way of going in circles, where I end up undoing and redoing changes as fixing one thing breaks another, until I take a step back to find the proper algorithm by considering multiple inputs.
Of course, ymmv depending on how good your initial intuition is, and how tricky the problem is.
> With pytest you can use the -x flag to stop after the first test failure.
> Even better you can use that in combination with -lf to only run the last failed test.
Fwiw `--sw` is much better for that specific use-case.
`--lf` is more useful to run the entire test suite, then re-run just the failed tests (of the entire suite). IIRC it can have some odd interactions with `-x` or `--maxfail`, because the strange things happen to the cached "selected set".
Though it may also be because I use xdist a fair bit, and the interaction of xdist with early interruptions (x, maxfail, ...) seems less than perfect.
