Some things are better with more code. Sets of data in particular (strongly typed). Sometimes you need to modify those sets of data and it doesn’t require enough work to write a whole script for, but you still don’t really want to spend the time manually modifying everything. LLM’s are really nice in those instances.
Plain data in all glory but I’d rather (deterministically) generate and modify it than having big blobs checked in. If I have too much copy pasted data I often forget to modify until the test case runs and I realize it. This creates a false sense of confidence since there could be tests that pass and nobody ever checks they’re wrong. Essentially, minimize the number of human steps to verify it looks right which typically means optimizing for the least amount of code.
Flashback to when I committed a suite of tests in Python that were indented one tab too much, resulting in them not running at all. This passed code review (on a FAANG company) and was discovered months later from an unrelated bug. The point is even unit tests have a very human element to them.
