Improving Automated Program Repair using Two-layer Tree-based Neural Networks

Document Type

Conference Proceeding

Publication Date

10-1-2020

Abstract

We present DLFix, a two-layer tree-based model learning bug-fixing code changes and their surrounding code context to improve Automated Program Repair (APR). The first layer learns the surrounding code context of a fix and uses it as weights for the second layer that is used to learn the bug-fixing code transformation. Our empirical results on Defect4J show that DLFix can fix 30 bugs and its results are comparable and complementary to the best performing pattern-based APR tools. Furthermore, DLFix can fix 2.5 times more bugs than the best performing deep learning baseline.

Identifier

85098569862 (Scopus)

ISBN

[9781450371223]

Publication Title

Proceedings 2020 ACM IEEE 42nd International Conference on Software Engineering Companion ICSE Companion 2020

External Full Text Location

https://doi.org/10.1145/3377812.3390896

First Page

316

Last Page

317

Grant

CCF-1518897

Fund Ref

National Science Foundation

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