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

arXiv: https://arxiv.org/abs/1911.03576 · HTML: https://mlsanity.com/paper/1911.03576/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Exploring ChatGPT's Capabilities on Vulnerability Management](https://mlsanity.com/paper/2311.06530/) (2023)
- [DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning](https://mlsanity.com/paper/2108.08041/) (2021)
- [Unsupervised Learning of General-Purpose Embeddings for Code Changes](https://mlsanity.com/paper/2106.02087/) (2021)
- [CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back](https://mlsanity.com/paper/2302.03924/) (2023)
- [LLM-Driven Kernel Evolution: Automating Driver Updates in Linux](https://mlsanity.com/paper/2511.18924/) (2025)

## Abstract (arXiv)

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.