Implement swarm testing for st.one_of
#4322
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| # this really messes up our deduplication tracking, because all 255 | ||
| # draws are unique. But we more or less have to choose whether something | ||
| # is enabled on-demand with a prior probability, rather than choosing what | ||
| # is enabled up front, because the latter results in a very large choice | ||
| # sequence when there are lots of possibilities. | ||
| # (a tradeoff might be selecting up front when there are <= 3 options?) | ||
| self.__p_disabled = self.__data.draw_integer(0, 254) / 255 |
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See comment; not too sure what to do here. Here's one example of this added redundancy:
@given(st.from_regex(re.compile("[ab]*", re.IGNORECASE), fullmatch=True))
def f(v):
print(v)
f()which prints "A" once on master but ~8 times on this branch.
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if self.__p_disabled is only ever used as the p= argument to draw_bool(), and is strictly between 0 and 1, do we need to choose it via the choice sequence at all? vs. data.random(), or 0.5 if that's not available?
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Sort of. If we don't draw this from the choice sequence, DataTree thinks the later is_disabled draw is flaky because the kwargs p passed to draw_boolean has changed. It's not actually flaky because the values in the choice sequence don't depend on p (as long as we're careful about p=0.0 and p=1), but DataTree doesn't know that.
We could imagine some kind of "ignore this param for flakiness" api, but I'm not sure how that would look.
Alternatively, hardcode that p in draw_boolean shouldn't be checked for flakiness? It's controlling the distribution, not the domain, so arguably consumers should be able to change it without flakiness...
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I think ignoring distribution-only arguments in flakiness-checks makes sense; we can still replay perfectly.
| self.enabled_branches_strategy = st.shared( | ||
| FeatureStrategy(self.original_strategies), | ||
| key=("one_of swarm testing", self.original_strategies), | ||
| ) | ||
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I think that using st.shared for the FeatureStrategy means that any separately-defined strategies which happen to have the same key will have the same swarm behavior. i.e. two separate usages of st.integers() | st.floats() will use the same swarm decision of what to disable. But I believe they will still draw separate random choices below the swarm step, so the values won't be the same, just the disabling decision.
I think diversity of whether to share swarm decisions here would be best for bug finding. What we might be able to do is draw a meta p which chooses, for each occurrence of the same-keyed strategy s, whether to reuse swarm behavior or not. But I don't know if this would be easy to implement.
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sounds complicated; let's leave that for a follow-up PR.
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This is a core change to our generation logic - which we also just changed in #4356. Since I'm not confident this PR will be regression free (in either performance or bugfinding ability), I'm tempted to defer this PR for a bit to let any issues with the constants pool shake out first |
Part of #2643. I think swarm testing is great and would love to get it into more places in Hypothesis. There are still some problems with this implementation (see review comments).