Nice! I like your solution, even though my brain isn't in the mood for attempting to juggle that right now :)

I just made a new translation for Julia :-)

Cheers!

Julia version does not call the reference solution in the random tests. Instead, the user's solution is called to verify itself.

Nice! I was unaware of this lazy version of filter

Approved, thanks!

Kotlin translation (direct translation of my Java version)

Ah, okay. It was interesting to know.

Writing a program to verify parenthesis is one of the most classic tasks in programming, that would be like accusing a task "generate a list of prime numbers" as being stolen. Not to mention that the kata is a remake of this kata which was created in 2013, while the earliest similar task I can find on Leetcode is from 2015. Are you sure they didn't "steal" from us? ;)

Stolen from Leetcode

Thanks, approved!

Looks good.

Julia Fork with a couple of enhancements. The iterate definition is more efficient and the implementation is cleaned up in general. Also, since the new version of FactCheck no longer shows the custom message for a positive result, I've changed things to be slightly more meaningful to the user (e.g. 1 --> 1 now reads Passed --> true for a successful test set)

Nothing in here that is critical. I just wanted to improve the iterate method and figured I'd touch up other things while I was at it, so feel free to ignore.

Somewhat off-topic, but I really like to read detailed and insightful exchanges like this one. If you power users could make more of it, it would be really great (to me, at least).

I have to admit that I generally agree with Kacarott here, but I do greatly value the insight from geoffp. Indeed, I used it to reconfigure the testset on the kata I mentioned above that I had made and given up on. I think now it could work, so I republished it, albeit with a variation of the ideas that we've been discussing. Namely I used a set of favorable seeds from which one is chosen at random to run the testset. The seeds take out the extreme sensitivity of the algorithm to differring inputs, which allows the alternative algo to consistantly fail.

It's still not the strongest way I could make the tests, but I think it could be a good alternative to what I had before, while still requiring relatively little extra time in test production.