Improving automated program repair using two-layer tree-based neural networks

Yi Li, Shaohua Wang, Tien N. Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering
Subtitle of host publicationCompanion Proceedings, ICSE-Companion 2020
PublisherIEEE Computer Society
Pages316-317
Number of pages2
ISBN (Electronic)9781450371223
DOIs
StatePublished - Jun 27 2020
Event42nd ACM/IEEE International Conference on Software Engineering, ICSE-Companion 2020 - Virtual, Online, Korea, Republic of
Duration: Jun 27 2020Jul 19 2020

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257

Conference

Conference42nd ACM/IEEE International Conference on Software Engineering, ICSE-Companion 2020
Country/TerritoryKorea, Republic of
CityVirtual, Online
Period6/27/207/19/20

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Automated Program Repair
  • Deep Learning

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