  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.
