Dlfix: Context-based code transformation learning for automated program repair

Li Yi, Shaohua Wang, Tien N. Nguyen

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

48 Scopus citations

Abstract

Automated Program Repair (APR) is very useful in helping developers in the process of software development and maintenance. Despite recent advances in deep learning (DL), the DL-based APR approaches still have limitations in learning bug-fixing code changes and the context of the surrounding source code of the bug-fixing code changes. These limitations lead to incorrect fixing locations or fixes. In this paper, we introduce DLFix, a two-tier DL model that treats APR as code transformation learning from the prior bug fixes and the surrounding code contexts of the fixes. The first layer is a tree-based RNN model that learns the contexts of bug fixes and its result is used as an additional weighting input for the second layer designed to learn the bug-fixing code transformations. We conducted several experiments to evaluate DLFix in two benchmarks: Defect4j and Bugs.jar, and a newly built bug datasets with a total of +20K real-world bugs in eight projects. We compared DLFix against a total of 13 state-of-the-art pattern-based APR tools. Our results show that DLFix can auto-fix more bugs than 11 of them, and is comparable and complementary to the top two pattern-based APR tools in which there are 7 and 11 unique bugs that they cannot detect, respectively, but we can. Importantly, DLFix is fully automated and data-driven, and does not require hard-coding of bug-fixing patterns as in those tools. We compared DLFix against 4 state-of-the-art deep learning based APR models. DLFix is able to fix 2.5 times more bugs than the best performing baseline.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering, ICSE 2020
PublisherIEEE Computer Society
Pages602-614
Number of pages13
ISBN (Electronic)9781450371216
DOIs
StatePublished - Jun 27 2020
Event42nd ACM/IEEE International Conference on Software Engineering, ICSE 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 2020
Country/TerritoryKorea, Republic of
CityVirtual, Online
Period6/27/207/19/20

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Automated program repair
  • Context-based code transformation learning
  • Deep learning

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