PatchNet: Hierarchical Deep Learning-Based Stable Patch Identification for the Linux Kernel

TL;DR

This paper introduces PatchNet, a hierarchical deep learning model that automatically identifies stable patches in the Linux kernel, outperforming previous methods by leveraging code and commit message features.

Contribution

PatchNet is a novel hierarchical deep learning approach that improves stable patch identification by automatically extracting features from code and commit messages.

Findings

PatchNet outperforms state-of-the-art baselines.

Deep hierarchical structure captures code semantics effectively.

Automates stable patch detection with high accuracy.

Abstract

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.