A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
This addresses the challenge of feature engineering for neural code intelligence models, but it appears incremental as it applies an existing concept to a new domain.
The paper tackles the problem of improving neural models of code by exploring the impact of unsupervised feature enrichment based on variable roles, finding that it can enhance performance, though specific numerical gains are not detailed in the abstract.
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.