“No Duh,” say senior developers everywhere.
The article explains that vibe code often is close, but not quite, functional, requiring developers to go in and find where the problems are - resulting in a net slowdown of development rather than productivity gains.
The reason tests are a good candidate is that there is a lot of boilerplate and no complicated business logic. It can be quite a time saver. You probably know some untested code in some project - you could get an llm to write some tests that would at least poke some key code paths, which is better than nothing. If the tests are wrong, it’s barely worse than having no tests.
Wrong tests will make you feel safe. And in the worst case, the next developer that is going to port the code will think that somebody wrote those tests with intention, and potentially create broken code to make the test green.
Exactly! I’ve seen plenty of tests where the test code was confidently wrong and it was obvious the dev just copied the output into the assertion instead of asserting what they expect the output to be. In fact, when I joined my current org, most of the tests were snapshot tests, which automated that process. I’ve pushed to replace them such with better tests, and we caught bugs in the process.
Then write comments in the tests that say they haven’t been checked.
That is indeed the absolute worst case though, and most of the tests that are so produced will be giving value because checking a test is easier than checking the code (this is kind of the point of tests) and so most will be correct.
The risk of regressions covered by the good tests is higher than someone writing code to the rare bad test that you’ve marked as suspicious because you (for whatever reason) are not confident in your ability to check it.
I disagree. I’d much rather have a lower coverage with high quality tests than high coverage with dubious tests.
If your tests are repetitive, you’re probably writing your tests wrong, or at least focusing on the wrong logic to test. Unit tests should prove the correctness of business logic and calculations. If there’s no significant business logic, there’s little priority for writing a test.
The actual risk of those tests being wrong is low because you’re checking them.
If your tests aren’t repetitive they’ve got no setup or mocking in so they don’t test very much.
If your test code is repetitive, you’re not following DRY sufficiently, or the code under test is overly complicated. We’ll generally have a single mock or setup code for several tests, some of which are parameterized. For example, in Python:
@parameterized.expand([ (key, value, Expected Exception,), (other_key, other_value, OtherExpectedException,), ]) def test_exceptions(self, key, value, exception_class): obj = setup() setattr(obj, key, value) with self.assertRaises(exception_class): func_to_test(obj)Mocks are similarly simple:
@unittest.mock.patch.object(Class, "method", return_value=...) dynamic_mock = MagicMock(Class) dynamic_mock...How this looks will vary in practice, but the idea is to design code such that usage is simple. If you’re writing complex mocks frequently, there’s probably room for a refactor.
I know how to use parametrised tests, but thanks.
Tests are still much more repetitive than application code. If you’re testing a wrapper around some API, each test may need you to mock a different underlying API call. (Mocking all of them at once would hide things). Each mock is different, so you can’t just extract it somewhere; but it is still repetitive.
If you need three tests each of which require a (real or mock) user, a certain directory structure to be present somewhere, input data to be got from somewhere, that’s three things that, even if you streamline them, need to be done in each test. I have been involved in a project where we originally followed the principle of, “if you need a user object in more than one test, put it in
setUpor in a shared fixture” and the result is rapid unwieldy shared setup between tests - and if ever you should want to change one of those tests, you’d better hope you only need to add to it, not to change what’s already there, otherwise you break all the other tests.For this reason, zealous application of DRY is not a good idea with tests, and so they are a bit repetitive. That is an acceptable trade-off, but also a place where an LLM can save you some time.
Ah, the end of all coding discussions, “if this is a problem for you, your code sucks.” I mean, you’re not wrong, because all code sucks.
LLMs are like the junior dev. You have to review their output because they might have screwed up in some stupid way, but that doesn’t mean they’re not worth having.
I absolutely agree. My point is that if you need complex setup, there’s a good chance you can reuse it and replace only the data that’s relevant for your test instead of constructing it every time.
But yes, there’s a limit here. We currently have a veritable mess because we populate the database with fixture data so we have enough data to not need setup logic for each test. Changing that fixture data causes a dozen tests to fail across suites. Since I started at this org, I’ve been pushing against that and introduced the repository pattern so we can easily mock db calls.
IMO, reused logic/structures should be limited to one test suite. But even then, rules are meant to be broken, just make sure you justify it.
I’m still not convinced that’s the case though. A typical mock takes a minute or two to write, most of the time is spent thinking about which cases to hit or refactoring code to make testing easier. Working with the LLM takes at least that long, esp if you count reviewing the generated code and whatnot.
Right, and I don’t want a junior dev writing my tests. Junior devs are there to be trained with the expectation that they’ll learn from mistakes. LLMs don’t learn, they’re perennially junior.
That’s why I don’t use them for code gen and instead use them for research. Writing code is the easy part of my job, knowing what to write is what takes time, so I outsource as much of the latter as I can.