Regardless, it is interesting to think about what domains are easier to generate effective models for. I would expect it to be easier to generate a supervised model <test description> => <test code>. My intuition is also, that it is easier to generate React component code, and harder to generate feature code.
And was all over the tech news at the time.
I kind of suspect you are intentionally trolling…
What was ruled by the supreme court was that Google's usage of the API (which had already determined to be copyrighted) fell under fair use in copyright law.
Kind of; the appeal denied was an interlocutory appeal (an appeal before final judgement), so the lower court ruling was left in place until final resolution of the case potentially to be settled in any final appeal.
However, while copyrightability of APIs was raised on the final appeal, the Supreme Court sidestepped it, ruling that because Google’s use was fair use even if the API was copyrightable, it was unnecessary to decide the copyrightability question at all. So the Federal Circuit decision remains in place on copyrightability.
On the gripping hand, though, that decision really doesn't matter much because Federal Circuit decisions on issues outside of those it has unique appellate responsibility aren’t binding precedent on trial courts (it is supposed to apply the case law of the geographic circuit that would otherwise apply, but its rulings don’t have the precedential effect that rulings of that circuit would have.)
So, basically, as far as appellate case law, Oracle v. Google provides no binding precedent on copyrightability of APIs, but precedent that at least one pattern of API copying is fair use if APIs are copyrightable.
Which isn’t encouraging for anyone looking to protect an API with copyrights.
It bases this generated test cases on other similar test cases in other software, including GPL licensed
This, so much. My code since using Copilot is easily ten times better tested than it was before, and I wasn't especially lazy when it comes to testing.
Given 1-2 hand-written unit tests, Copilot can start filling in test bodies that correctly test what's described in the function name. When I can't think of any more edge cases, I'll go prompt it with one more @Test annotation (or equivalent in another language) and it will frequently come up with edge cases that I didn't even think of and write a test that tests that edge case.
(One great part about this use case for those who are a little antsy about the copyright question is that you can be pretty darn confident that you're not running a risk of accidental copyright violation. I write the actual business logic by hand, which means copilot is generating tests that only interact with an API that I wrote.)