# PatchNet: A Tool for Deep Patch Classification

## TL;DR

PatchNet is a hierarchical deep learning tool designed to classify software patches by analyzing commit messages and code changes, aiming to automate patch relevance identification.

## Contribution

It introduces a novel hierarchical deep learning architecture tailored for patch classification, outperforming existing models by capturing the structure of code changes.

## Key findings

- Successfully identified relevant patches in the Linux kernel
- Provides customizable parameters for training
- Potentially applicable to various software engineering tasks

## 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.