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

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICSE-Companion.2019.00044

Share

COinS