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pro vyhledávání: '"D. Q. Bui"'
Akademický článek
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Publikováno v:
Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering.
Despite the recent trend of developing and applying neural source code models to software engineering tasks, the quality of such models is insufficient for real-world use. This is because there could be noise in the source code corpora used to train
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df9e14fcf43160b790416d6c0d7c43fc
https://doi.org/10.1145/3551349.3561168
https://doi.org/10.1145/3551349.3561168
Akademický článek
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Akademický článek
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We propose Corder, a self-supervised contrastive learning framework for source code model. Corder is designed to alleviate the need of labeled data for code retrieval and code summarization tasks. The pre-trained model of Corder can be used in two wa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a066b53fc44d2ea7781d74b9549541d5
http://arxiv.org/abs/2009.02731
http://arxiv.org/abs/2009.02731
Autor:
Nghi D. Q. Bui, Lingxiao Jiang, Md. Rafiqul Islam Rabin, Yijun Yu, Mohammad Amin Alipour, Ke Wang
With the prevalence of publicly available source code repositories to train deep neural network models, neural program models can do well in source code analysis tasks such as predicting method names in given programs that cannot be easily done by tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::96b15e9b1889bcc58bf7df968c1bb4c1
http://arxiv.org/abs/2008.01566
http://arxiv.org/abs/2008.01566
Publikováno v:
2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)
ICSE
ICSE
Building deep learning models on source code has found many successful software engineering applications, such as code search, code comment generation, bug detection, code migration, and so on. Current learning techniques, however, have a major drawb
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::888b127a638bd8600f182f7549121521
Publikováno v:
ASE
The 34th IEEE/ACM International Conference on Automated Software Engineering (ASE 2019)
The 34th IEEE/ACM International Conference on Automated Software Engineering (ASE 2019)
Despite being adopted in software engineering tasks, deep neural networks are treated mostly as a black box due to the difficulty in interpreting how the networks infer the outputs from the inputs. To address this problem, we propose AutoFocus, an au
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9fccfa9e2d3f527086006a8fbcbf435d
https://doi.org/10.1109/ase.2019.00014
https://doi.org/10.1109/ase.2019.00014
Autor:
Nghi D. Q. Bui
Publikováno v:
ICSE (Companion Volume)
Programmers often need to migrate programs from one language or platform to another in order to implement functionality, instead of rewriting the code from scratch. However, most techniques proposed to identify API mappings across languages and facil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::84e8d0c83e9a6bac5205bd2dfb92ccbe
https://doi.org/10.1109/icse-companion.2019.00054
https://doi.org/10.1109/icse-companion.2019.00054
Publikováno v:
SANER
The 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering
The 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering
Algorithm classification is to automatically identify the classes of a program based on the algorithm(s) and/or data structure(s) implemented in the program. It can be useful for various tasks, such as code reuse, code theft detection, and malware de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::979561d36200d254303a78ec29217191
https://doi.org/10.1109/saner.2019.8667995
https://doi.org/10.1109/saner.2019.8667995