Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2023

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. [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.

Keywords

neural models of code, feature engineering, unsuperivsed feature enrichment approach

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2023 IEEE/ACM International Workshop on Interpretability and Robustness in Neural Software Engineering (InteNSE)

First Page

14

Last Page

20

ISBN

979-8-3503-0172-4

Identifier

10.1109/InteNSE59150.2023.00007

Publisher

IEEE

City or Country

Melbourne

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