Only if you are missing tests for what counts for you. And that's true for both dev-written code, and for vibed code.
Only if you are missing tests for what counts for you. And that's true for both dev-written code, and for vibed code.
The defining feature of vibe coding is that the human prompter doesn't know or care what the actual code looks like. They don't even try to understand it.
You might instruct the LLM to add test cases, and even tell it what behavior to test. And it will very likely add something that passes, but you have to take the LLM's word that it properly tests what you want it to.
I was writing about vibe-coding. It seems these guys are vibe-coding (https://factory.strongdm.ai/) and their LLM coders write the tests.
I've seen this in action, though to dubious results: the coding (sub)agent writes tests, runs them (they fail), writes the implementation, runs tests (repeat this step and last until tests pass), then says it's done. Next, the reviewer agent looks at everything and says "this is bad and stupid and won't work, fix all of these things", and the coding agent tries again with the reviewer's feedback in mind.
Models are getting good enough that this seems to "compound correctness", per the post I linked. It is reasonable to think this is going somewhere. The hard parts seem to be specification and creativity.
The setups I've seen use subagents to handle coding and review, separately from each other and from the "parent" agent which is tasked with implementing the thing. The parent agent just hands a task off to a coding agent whose only purpose is to do the task, the review agent reviews and goes back and forth with the coding agent until the review agent is satisfied. Coding agents don't seem likely to suffer from this particular failure mode.