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- W4229071782 abstract "In recent years, the rise of deep learning and automation requirements in the software industry has elevated Intelligent Software Engineering to new heights. The number of approaches and applications in code understanding is growing, with deep learning techniques being used in many of them to better capture the information in code data. In this survey, we present a comprehensive overview of the structures formed from code data. We categorize the models for understanding code in recent years into two groups: sequence-based and graph-based models, further make a summary and comparison of them. We also introduce metrics, datasets and the downstream tasks. Finally, we make some suggestions for future research in structural code understanding field." @default.
- W4229071782 created "2022-05-08" @default.
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- W4229071782 date "2022-05-02" @default.
- W4229071782 modified "2023-10-18" @default.
- W4229071782 title "A Survey of Deep Learning Models for Structural Code Understanding" @default.
- W4229071782 doi "https://doi.org/10.48550/arxiv.2205.01293" @default.
- W4229071782 hasPublicationYear "2022" @default.
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