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Comment by olex | Hacker News Reader

This seems like a test design issue to me. Best practice is to avoid for-each loops with assertions within tests - using parametrized tests and feeding the looped values as input is almost always a better option. Figuring out which one failed and why is one advantage it gives you in comparison. Another one is that all inputs will always be tested - your example stops on the first one that fails, and does not evaluate the others after that.

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not really. one thing this is useful for is extracting out various attributes in an object when you really don't want to compare the entire thing. Or comparing dict attributes, and figuring which one is the incorrect one.

for example,

    expected_results = {...}
    actual_obj = some_intance.method_call(...)

    for key, val in expected_results.items():
        assert getattr(actual_obj, key) == val, f"Mismatch for {key} attribute"

You could shift this off to a parametrized test, but that means you're making N more calls to the method being tested, which can have its own issues with cost of test setup and teardown. With this method, you see which key breaks, and re-run after fixing.

Ok, in this case a parametrized test is not the best approach, I agree. But I would still want to avoid the for-each and "failing fast". One approach would be to gather the required attributes in an array or a struct of some sort, and then do a single assert comparison with an expected value, showing all the differences at once. However, this requires the assertion framework to be able to make such a comparison and return a nicely readable error message, ideally with a diff.

Right, and not many actually do. with python and pytest, you could leverage difflib, but that's an additional thing that adds unnecessary complexity. My approach is simple enough, good enough, and doesn't require additional fudging around with the basics of the language's test libs.

also,

>your example stops on the first one that fails, and does not evaluate the others after that.

I would argue this is desirable behavior. there are soft checks, ie, https://pypi.org/project/pytest-check/, that basically replace assertions as raised exceptions and do your approach. But I do want my tests to raise errors at the point of failure when a change occurs. If there's alot of changes occurring, that raises larger questions of "why" and "is the way we're executing this change a good one"?

> your example stops on the first one that fails, and does not evaluate the others after that.

I don't think this is a big problem; trying to focus on multiple examples at once is difficult.

It might be a problem if tests are slow and you are forced to work on all of them at once.

But in that case I'd try to make the tests faster (getting rid of network requests, disk/DB access by faking them away or hoisting to the caller).

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.

[0] - https://kata-log.rocks/tdd

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.

Again pytest makes things so much nicer in this regard. Having to comment things out sucks.

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.

> 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.

Oh nice, didn't know about that flag!

Another option is to use a custom mark on the test you want to run, and then do something like "pytest -v -m onlyrunthis"

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