PatchNet: A Tool for Deep Patch Classification
This tool addresses the need for automating patch classification in software engineering, particularly for tasks like identifying stable patches in the Linux kernel, but it appears incremental as it builds on existing deep learning models with a hierarchical structure.
The authors tackled the problem of automated patch classification by introducing PatchNet, a hierarchical deep learning tool that extracts features from commit messages and code changes, validated on identifying stable-relevant patches in the Linux kernel.
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.