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SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair
KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.ORCID iD: 0000-0002-6673-6438
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2019 (English)In: IEEE Transactions on Software Engineering, ISSN 0098-5589, E-ISSN 1939-3520Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a technique, called SEQUENCER, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate SEQUENCER on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SEQUENCER is able to perfectly predict the fixed line for 950/4,711 testing samples, and find correct patches for 14 bugs in Defects4J benchmark. SEQUENCER captures a wide range of repair operators without any domain-specific top-down design.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019.
Keywords [en]
machine learning, program repair, Defects, Learning systems, Open source software, Open systems, Data driven, Domain specific, Open source repositories, Repair operator, Sequence learning, Source codes, Testing samples, Top down design, Repair
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-263253DOI: 10.1109/TSE.2019.2940179Scopus ID: 2-s2.0-85072171801OAI: oai:DiVA.org:kth-263253DiVA, id: diva2:1368167
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QC 20191106

Available from: 2019-11-06 Created: 2019-11-06 Last updated: 2019-11-06Bibliographically approved

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Chen, Zimin

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