Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2019
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 selectparameters 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.
Keywords
Deep learning, Patch classification, Stable patch identification
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 41st ACM/IEEE International Conference on Software Engineering (ICSE 2019), Montreal, Canada, 2019 May 25-31
First Page
83
Last Page
86
ISBN
9781728117645
Identifier
10.1109/ICSE-Companion.2019.00044
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
HOANG, Thong; LAWALL, Julia; OENTARYO, Richard J.; TIAN, Yuan; and LO, David.
PatchNet: A tool for deep patch classification. (2019). Proceedings of the 41st ACM/IEEE International Conference on Software Engineering (ICSE 2019), Montreal, Canada, 2019 May 25-31. 83-86.
Available at: https://ink.library.smu.edu.sg/sis_research/4477
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICSE-Companion.2019.00044