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I've found the same, the rate of bugs has dropped pretty dramatically after switching to ai generated code. I think it's partly because ai will write 1000s of lines of unit tests without complaining. I also have a github workflow where claude runs the /code-review command on every PR.

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In the world of web apps, I find the agent's ability to write good e2e tests to be a real game changer. Turns out with enough rigor you can write pretty stable mostly not flaky e2e tests. And even flaky ones are fixed quickly due to a fuck ton of assertions at every step.

Test code looks like a mess, even more than the usual LLM code. takes a while to let it go. Test report looks beautiful though.

When the test code "looks like a mess", how do you get assurance it is testing the right properties?

Like you, I've found that LLMs can improve test coverage by decreasing the amount of developer time spent writing tests. But generally, it takes a lot of manual work to set up the initial testing framework, and even then, a lot of vigilance to ensure that what is actually tested corresponds to the description of the test.

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