论文标题
对结构代码理解的深度学习模型的调查
A Survey of Deep Learning Models for Structural Code Understanding
论文作者
论文摘要
近年来，软件行业的深度学习和自动化要求的兴起将智能软件工程提升到了新的高度。代码理解中的方法和应用程序的数量正在增长，其中许多人都使用深度学习技术来更好地捕获代码数据中的信息。在这项调查中，我们介绍了由代码数据形成的结构的全面概述。我们将近年来将代码理解的模型分为两组：基于序列和基于图的模型，进一步摘要和比较。我们还介绍指标，数据集和下游任务。最后，我们为未来的结构守则理解领域的研究提出了一些建议。
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.
