@inproceedings{48eff8537b4e4c6b9e705ab594d99a8a,
title = "Improving automated program repair using two-layer tree-based neural networks",
abstract = "We present DLFix, a two-layer tree-based model learning bug-fixingcode changes and their surrounding code context to improve Automated Program Repair (APR). The first layer learns the surroundingcode context of a fix and uses it as weights for the second layer thatis used to learn the bug-fixing code transformation. Our empiricalresults on Defect4J show that DLFix can fix 30 bugs and its resultsare comparable and complementary to the best performing patternbased APR tools. Furthermore, DLFix can fix 2.5 times more bugsthan the best performing deep learning baseline.",
keywords = "Automated Program Repair, Deep Learning",
author = "Yi Li and Shaohua Wang and Nguyen, {Tien N.}",
note = "Publisher Copyright: {\textcopyright} 2020 Copyright held by the owner/author(s).; 42nd ACM/IEEE International Conference on Software Engineering, ICSE-Companion 2020 ; Conference date: 27-06-2020 Through 19-07-2020",
year = "2020",
month = jun,
day = "27",
doi = "10.1145/3377812.3390896",
language = "English (US)",
series = "Proceedings - International Conference on Software Engineering",
publisher = "IEEE Computer Society",
pages = "316--317",
booktitle = "Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering",
address = "United States",
}