  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.
