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ICSE 2019
Sat 25 - Fri 31 May 2019 Montreal, QC, Canada
Fri 31 May 2019 12:00 - 12:20 at Centre-Ville - Mining Software Changes and Patterns Chair(s): Ayşe Başar

This work proposes PatchNet, an automated tool based on hierarchical deep learning for classifying patches by extracting features from log 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. Our video demonstration on the performance of PatchNet is publicly available at

Fri 31 May

11:00 - 12:30: Papers - Mining Software Changes and Patterns at Centre-Ville
Chair(s): Ayşe BaşarRyerson University
icse-2019-Technical-Papers11:00 - 11:20
Ralf RamsauerOTH Regensburg, Daniel LohmannLeibniz Universität Hannover, Wolfgang MauererOTH Regensburg / Siemens AG
icse-2019-Technical-Papers11:20 - 11:40
Hoan NguyenIowa State University, Tien N. NguyenUniversity of Texas at Dallas, Danny DigSchool of EECS at Oregon State University, Son NguyenThe University of Texas at Dallas, Hieu TranThe University of Texas at Dallas, Michael HiltonCarnegie Mellon University, USA
icse-2019-Demonstrations11:40 - 12:00
Matias MartinezUniversity of Valenciennes, Martin MonperrusKTH Royal Institute of Technology
icse-2019-Demonstrations12:00 - 12:20
Thong Hoang, Julia LawallInria/LIP6, Richard J OentaryoMcLaren Applied Technologies, Singapore, Yuan TianQueens University, Kingston, Canada, David LoSingapore Management University
icse-2019-Paper-Presentations12:20 - 12:30