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ICSE 2020
Wed 24 June - Thu 16 July 2020
Sat 11 Jul 2020 15:44 - 15:56 at Goguryeo - A26-Bugs and Repair Chair(s): Davide Falessi

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 learning which context of the surrounding source code that certain bug-fixing changes should be made. These limitations lead to incorrect fixing locations or incorrect 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, which is designed to learn the bug-fixing code transformations.

We conducted several experiments to evaluate DLFix in two standard datasets Defects4J, and Bugs.jar, and in a newly built bug datasets with a total of +20K real-world bugs in eight projects. We have compared against a total of 13 state-of-the-art pattern-based APR tools. Our results show that DLFix improves over 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.

Sat 11 Jul
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15:00 - 16:00: Paper Presentations - A26-Bugs and Repair at Goguryeo
Chair(s): Davide FalessiCalifornia Polytechnic State University
icse-2020-papers15:00 - 15:12
Mingyuan WuSouthern University of Science and Technology, Yicheng OuyangSouthern University of Science and Technology, Husheng ZhouThe University of Texas at Dallas, Lingming ZhangThe University of Texas at Dallas, Cong LiuUT Dallas, Yuqun ZhangSouthern University of Science and Technology
icse-2020-Journal-First15:12 - 15:20
Luciano BaresiPolitecnico di Milano, Alberto LevaPolitecnico di Milano, Giovanni QuattrocchiPolitecnico di Milano
icse-2020-Journal-First15:20 - 15:28
Carlos Gavidia-CalderonUniversity College London, Federica SarroUniversity College London, UK, Mark HarmanFacebook and University College London, Earl T. BarrUniversity College London, UK
Link to publication DOI Pre-print Media Attached
icse-2020-Journal-First15:28 - 15:36
Anil KoyuncuUniversity of Luxembourg, Luxembourg, Kui LiuNanjing University of Aeronautics and Astronautics, Tegawendé F. BissyandéSnT, University of Luxembourg, Dongsun KimFuriosa.ai, Jacques KleinUniversity of Luxembourg, SnT, Martin MonperrusKTH Royal Institute of Technology, Yves Le TraonUniversity of Luxembourg
icse-2020-Journal-First15:36 - 15:44
Paul MunteanTU Munich, Martin MonperrusKTH Royal Institute of Technology, Hao SunUnaffiliated, Jens GrossklagsTechnical University of Munich, Claudia EckertTechnical University of Munich
icse-2020-papers15:44 - 15:56
Yi LiNew Jersey Institute of Technology, USA, Shaohua WangNew Jersey Institute of Technology, USA, Tien N. NguyenUniversity of Texas at Dallas