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：尽管深度神经模型显著降低了特征工程的开销，但输入中现成的特征可能对训练成本和模型性能产生重大影响。本文探究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。Sajaniemi等人[参考文献1,2]提出的变量角色概念已被证明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改善代码神经模型的性能。据我们所知，这是首个研究Sajaniemi等人提出的变量角色概念如何影响代码神经模型的工作。具体而言，我们通过为数据集中程序的单个变量添加角色信息来增强源代码数据集，进而开展关于变量角色增强对Code2Seq模型训练影响的研究。此外，我们还揭示了面向神经代码智能模型的特征增强所面临的一些挑战与机遇。