UniGenCoder: Merging SEQ2SEQ and SEQ2TREE Paradigms for Unified Code Generation

Deep learning-based code generation has completely transformed the way developers write programs today. Existing approaches to code generation have focused either on the Sequence-to-Sequence paradigm, which generates target code as a sequence of tokens, or the Sequence-to-Tree paradigm, which output...

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Bibliographic Details

Published in:	IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Technologies Results (Online) pp. 71 - 75
Main Authors:	Shao, Liangying, Yan, Yanfu, Poshyvanyk, Denys, Su, Jinsong
Format:	Conference Proceeding
Language:	English
Published:	IEEE 27.04.2025
Subjects:	Code Generation Codes Contrastive learning Decoding Knowledge transfer Merging Multitasking Sequence-to-Sequence Sequence-to-Tree Software engineering Training
ISSN:	2832-7632
Online Access:	Get full text
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