Time-Efficient Code Completion Model for the R Programming Language

Artem Popov, Dmitrii Orekhov, Denis Litvinov, Nikolay Korolev, Gleb Morgachev


Abstract
In this paper we present a deep learning code completion model for the R language. We introduce several techniques to utilize language modeling based architecture in the code completion task. With these techniques, the model requires low resources, but still achieves high quality. We also present an evaluation dataset for the R language completion task. Our dataset contains multiple autocompletion usage contexts that provides robust validation results. The dataset is publicly available.
Anthology ID:
2021.nlp4prog-1.4
Volume:
Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | NLP4Prog
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
34–39
Language:
URL:
https://aclanthology.org/2021.nlp4prog-1.4
DOI:
10.18653/v1/2021.nlp4prog-1.4
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.nlp4prog-1.4.pdf
Code
 arti32lehtonen/rcompletion_evaluation_dataset