A Survey of Deep Learning Models for Structural Code Understanding
It addresses the need for automated code analysis in software engineering, but it is incremental as it synthesizes existing research without introducing new methods.
This survey provides a comprehensive overview of deep learning models for structural code understanding, categorizing them into sequence-based and graph-based approaches and summarizing their applications, metrics, datasets, and downstream tasks.
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.