I think the pros outweigh the cons -- I'll comment briefly on the PR. On Mon, 9 Sep 2019 at 02:41, Matti Picus <matti.picus@gmail.com> wrote:

We have discussed using the hypothesis package to generate test cases at a few meetings informally. At the EuroSciPy sprint, kitchoi took up the challenge and issued a pull request https://github.com/numpy/numpy/pull/14440 that actually goes ahead and does it. While not finding any new failures, the round-trip testing of s = np.array2string(np.array(s)) shows what hypothesis can do. The new test runs for about 1/2 a second. In my mind the next step would be to use this style of testing to expose problems in the np.chararray routines.

What do you think? Is the cost of adding a new dependency worth the more thorough testing?

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