[$] Identifying buggy patches with machine learning

The stable kernel releases are meant to contain as many important fixes as
possible; to that end, the stable maintainers have been making use of a
machine-learning system to identify patches that should be considered for a
stable update. This exercise has had some success but, at the 2019 Open
Source Summit Europe, Sasha Levin asked whether this process could be
improved further. Might it be possible for a machine-learning system to
identify patches that create bugs and intercept them, so that the
fixes never become necessary?