As more of an ex-Python committer, I have little to say about current processes. I will make one observation:
On Jan 20, 2017, at 11:45 AM, Victor Stinner firstname.lastname@example.org wrote:
I introduced a regression in random.Random.seed(): ...
IMHO the regression is not "catastrophic". Only few developers instanciate random.Random themself, random.Random must not be used for security, etc. I let others decide if this bug was catastrophic or not.
I do not like this comment. I feel like it has narrow understanding of who uses Python, of who who depends on this API, and the history of how important it is to have a good RNG seed even for non-security.
Scientific programming uses random numbers. A quick grep of the source code on my machine shows the following example from Biopython's Bio/GA/Mutation/Simple.py (comments and docstrings removed for clarity):
class SinglePositionMutation(object): def __init__(self, mutation_rate=0.001): self._mutation_rate = mutation_rate self._mutation_rand = random.Random() self._switch_rand = random.Random() self._pos_rand = random.Random()
If I understand the bug correctly, under Python 3.6 under Windows these will likely have the same seed. This may (or may not) affect downstream statistics. This may (or may not) require a publication to be retracted. This may (or may not) be a disaster to the researcher who now has to re-do all of the calculations and see if there is a problem.
It's certainly hard to track down all the people who might be affected and beg for their forgiveness, which is what it sounds like you will be doing if you really believe your development philosophy, and aren't using it as an excuse to skimp on testing and coordination.
Back in the 1990s there were a number of papers showing the importance of having a good RNG for physical simulations, even if it isn't cryptographically strong, and the importance of having no correlations between multiple RNGs. Basically, most programmers aren't good enough to identify bad default RNGs and seeds. The general solution put a good-enough default solution in the underlying language implementations. Which all modern languages, including Python, have done. Well, except now.
I do not think it's relevant to comment that only a few developers call Random() directly. The guideline should be the number of people who might be affected. There are many more Biopython users than developers, making the actual severity many times larger than that developer comment implies.
Finally, there's a long history of small, seemingly orthogonal changes in RNG code making large reductions in entropy, such as https://en.wikinews.org/wiki/Predictable_random_number_generator_discovered_... caused by following suggestions made by Valgrind and Purify; the sort that might be called "trivial (short, non controversal)". Anyone touching RNG code has to be aware that it's a sensitive area, and pay extra attention to testing.
In the bug report at http://bugs.python.org/issue29085 you write "Too bad that there is no simple way to write an unit test for that."
Here's a possible test based on the reproducible in the report:
Random = random.Random for i in range(1000): rng1 = Random() rng2 = Random() assert rng1.getrandbits(128) != rng2.getrandbits(128), "RNG initialization failed"
(I do not have a Windows box to test this. It passes 500,000 tests on my Mac.)
If the two Random() calls are in the same clock quantum and so get the same seed then they will have the same bit pattern. The odds of two independent seeds giving the same 128-bit pattern is incredibly low.
For this one bug, I agree with the interpretation that it was handled with a cavalier attitude. I don't feel like it's being treated with the seriousness it should. As far as I can tell there isn't even a regression to prevent something like this from happening again, only a handwaving that such a test is not simple (and why does a unit test for an important requirement have to be simple in order to be added?).
I've forwarded this Python 3.6 bug to the Biopython developers, at https://github.com/biopython/biopython/issues/1044 . I don't think it's a serious problem, but don't know enough to be certain. In any case, the likely easy fix for them is to reuse the same RNG instead of creating independent ones. However, I expect there are other projects which will be more affected by this bug.