Skip to Main content Skip to Navigation
Conference papers

PatchNet: A Tool for Deep Patch Classification

Abstract : 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [14 references]  Display  Hide  Download

https://hal.inria.fr/hal-02408347
Contributor : Julia Lawall <>
Submitted on : Friday, December 13, 2019 - 3:01:37 AM
Last modification on : Tuesday, March 23, 2021 - 9:28:03 AM
Long-term archiving on: : Saturday, March 14, 2020 - 1:33:12 PM

File

2019_ICSE_PatchNetTool_paper.p...
Files produced by the author(s)

Identifiers

Citation

Thong Hoang, Julia Lawall, Richard J. Oentaryo, Yuan Tian, David Lo. PatchNet: A Tool for Deep Patch Classification. ICSE-Companion 2019 - IEEE/ACM 41st International Conference on Software Engineering, May 2019, Montreal, Canada. pp.83-86, ⟨10.1109/ICSE-Companion.2019.00044⟩. ⟨hal-02408347⟩

Share

Metrics

Record views

93

Files downloads

626