PatchNet: Hierarchical Deep Learning-Based Stable Patch Identification for the Linux Kernel
This addresses the need for more reliable automation in maintaining stable Linux kernel versions, which is crucial for users prioritizing stability, though it is an incremental improvement over prior methods.
The authors tackled the problem of automatically identifying stable patches for the Linux kernel, proposing PatchNet, a hierarchical deep learning approach that achieved superior accuracy compared to existing methods, including one adopted by kernel maintainers, as validated on 82,403 patches.
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.