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 https://goo.gl/CZjG6X.
Fri 31 May
11:00 - 12:30: Papers - Mining Software Changes and Patterns at Centre-Ville Chair(s): Ayşe BaşarRyerson University | ||||||||||||||||||||||||||||||||||||||||||
11:00 - 11:20 Talk | Ralf RamsauerOTH Regensburg, Daniel LohmannLeibniz Universität Hannover, Wolfgang MauererOTH Regensburg / Siemens AG | |||||||||||||||||||||||||||||||||||||||||
11:20 - 11:40 Talk | 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 | |||||||||||||||||||||||||||||||||||||||||
11:40 - 12:00 Talk | ||||||||||||||||||||||||||||||||||||||||||
12:00 - 12:20 Talk | Thong Hoang, Julia LawallInria/LIP6, Richard J OentaryoMcLaren Applied Technologies, Singapore, Yuan TianQueens University, Kingston, Canada, David LoSingapore Management University | |||||||||||||||||||||||||||||||||||||||||
12:20 - 12:30 Talk |