  Linux kernel stable versions serve the needs of users who value stability of
the kernel over new features. The quality of such stable versions depends on
the initiative of kernel developers and maintainers to propagate bug fixing
patches to the stable versions. Thus, it is desirable to consider to what
extent this process can be automated. A previous approach relies on words from
commit messages and a small set of manually constructed code features. This
approach, however, shows only moderate accuracy. In this paper, we investigate
whether deep learning can provide a more accurate solution. We propose
PatchNet, a hierarchical deep learning-based approach capable of automatically
extracting features from commit messages and commit code and using them to
identify stable patches. PatchNet contains a deep hierarchical structure that
mirrors the hierarchical and sequential structure of commit code, making it
distinctive from the existing deep learning models on source code. Experiments
on 82,403 recent Linux patches confirm the superiority of PatchNet against
various state-of-the-art baselines, including the one recently-adopted by Linux
kernel maintainers.
