SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations

Recent years have seen the successful application of large pretrained models to code representation learning, resulting in substantial improvements on many code-related downstream tasks. But there are issues surrounding their application to SE tasks. First, the majority of the pre-trained models foc...

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Veröffentlicht in:	2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) S. 01 - 13
Hauptverfasser:	Niu, Changan, Li, Chuanyi, Ng, Vincent, Ge, Jidong, Huang, Liguo, Luo, Bin
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 01.05.2022
Schlagworte:	code representation learning Codes Computer architecture Decoding Natural languages pre-training Representation learning sequence-to-sequence Task analysis Transformers
ISSN:	1558-1225
Online-Zugang:	Volltext
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