Although deep neural models substantially reduce the overhead of feature engineering, the features readily available in the inputs might significantly impact training cost and the performance of the models. In this paper, we explore the impact of an unsuperivsed feature enrichment approach based on variable roles on the performance of neural models of code. The notion of variable roles (as introduced in the works of Sajaniemi et al. [Refs. 1,2]) has been found to help students' abilities in programming. In this paper, we investigate if this notion would improve the performance of neural models of code. To the best of our knowledge, this is the first work to investigate how Sajaniemi et al.'s concept of variable roles can affect neural models of code. In particular, we enrich a source code dataset by adding the role of individual variables in the dataset programs, and thereby conduct a study on the impact of variable role enrichment in training the Code2Seq model. In addition, we shed light on some challenges and opportunities in feature enrichment for neural code intelligence models.

翻译：尽管深度神经网络模型大幅降低了特征工程的开销，但输入中现成的特征仍可能显著影响训练成本和模型性能。本文探索了一种基于变量角色的无监督特征增强方法对代码神经模型性能的影响。变量角色的概念（由Sajaniemi等人提出[参考文献1,2]）已被证实有助于提升学生的编程能力。本文旨在研究这一概念是否能够改进代码神经模型的性能。据我们所知，这是首次探究Sajaniemi等人的变量角色概念如何影响代码神经模型的工作。具体而言，我们通过在数据集的程序中添加单个变量的角色信息来增强源代码数据集，进而开展变量角色增强对Code2Seq模型训练影响的实证研究。此外，我们揭示了神经代码智能模型特征增强领域面临的若干挑战与机遇。