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dc.contributor.authorHuang, Shanen_US
dc.contributor.authorZhou, Xiaoen_US
dc.contributor.authorChin, Sangen_US
dc.coverage.spatialSwitzerlanden_US
dc.date2021-01-08
dc.date.accessioned2021-11-09T18:48:09Z
dc.date.available2021-11-09T18:48:09Z
dc.date.issued2021
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/33817628
dc.identifier.citationS. Huang, X. Zhou, S. Chin. 2021. "Application of Seq2Seq Models on Code Correction.." Front Artif Intell, Volume 4, pp. 590215 - ?. https://doi.org/10.3389/frai.2021.590215
dc.identifier.issn2624-8212
dc.identifier.urihttps://hdl.handle.net/2144/43315
dc.description.abstractWe apply various seq2seq models on programming language correction tasks on Juliet Test Suite for C/C++ and Java of Software Assurance Reference Datasets and achieve 75% (for C/C++) and 56% (for Java) repair rates on these tasks. We introduce pyramid encoder in these seq2seq models, which significantly increases the computational efficiency and memory efficiency, while achieving similar repair rate to their nonpyramid counterparts. We successfully carry out error type classification task on ITC benchmark examples (with only 685 code instances) using transfer learning with models pretrained on Juliet Test Suite, pointing out a novel way of processing small programming language datasets.en_US
dc.format.extentp. 590215en_US
dc.languageeng
dc.language.isoen_US
dc.relation.ispartofFront Artif Intell
dc.rightsCopyright © 2021 Huang, Zhou and Chin.. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleApplication of seq2seq models on code correctionen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.3389/frai.2021.590215
pubs.elements-sourcepubmeden_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.publication-statusPublished onlineen_US
dc.identifier.mycv526324


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Copyright © 2021 Huang, Zhou and Chin.. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Except where otherwise noted, this item's license is described as Copyright © 2021 Huang, Zhou and Chin.. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.