IEEE transactions on software engineering, 2021-09, Vol.47 (9), p.1943-1959
2021
Details
- Autor(en) / Beteiligte
- Titel
- SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
- Ist Teil von
- IEEE transactions on software engineering, 2021-09, Vol.47 (9), p.1943-1959
- Ort / Verlag
- New York: IEEE
- Erscheinungsjahr
- 2021
- Link zum Volltext
- Quelle
- IEL
- Beschreibungen/Notizen
- 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0098-5589, 1939-3520eISSN: 1939-3520DOI: 10.1109/TSE.2019.2940179Titel-ID: cdi_swepub_primary_oai_DiVA_org_kth_263253
- Format
- –
- Schlagworte
- Benchmark testing, Benchmarks, Computer bugs, Data driven, Defects, Domain specific, Learning, Learning systems, Machine learning, Maintenance engineering, Natural languages, Open source repositories, Open source software, Open systems, Program repair, Repair, Repair operator, Sequence learning, Source code, Source codes, Testing samples, Top down design, Training, Vocabulary