PatchNet: A Tool for Deep Patch Classification
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Abstract (arXiv)
This work proposes PatchNet, an automated tool based on hierarchical deep learning for classifying patches by extracting features from commit messages and code changes. PatchNet contains a deep hierarchical structure that mirrors the hierarchical and sequential structure of a code change, differentiating it from the existing deep learning models on source code. PatchNet provides several options allowing users to select parameters for the training process. The tool has been validated in the context of automatic identification of stable-relevant patches in the Linux kernel and is potentially applicable to automate other software engineering tasks that can be formulated as patch classification problems. A video demonstrating PatchNet is available at https://goo.gl/CZjG6X. The PatchNet implementation is available at https://github.com/hvdthong/PatchNetTool.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Feb 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 1; 2026: 3.
5 papers.
- 2021Energy-bounded Learning for Robust Models of Codecited by 1
- 2025What Do They Fix? LLM-Aided Categorization of Security Patches for Critical Memory Bugscited by 1
- 2026Favia: Forensic Agent for Vulnerability-fix Identification and Analysiscited by 1
- 2026Stylometric Defenses Against Author Impersonation in Software Repositoriescited by 0
- 2026Exploring Semantic Stability Across Reviews in the Linux Kernelcited by 0
- 2026Exploring Semantic Stability Across Reviews in the Linux Kernel10 Aug 2026
- 2026Stylometric Defenses Against Author Impersonation in Software Repositories3 Aug 2026
- 2026Favia: Forensic Agent for Vulnerability-fix Identification and Analysis13 Feb 2026
- 2025What Do They Fix? LLM-Aided Categorization of Security Patches for Critical Memory Bugs26 Sep 2025
- 2021Energy-bounded Learning for Robust Models of Code20 Dec 2021