A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code

Nadezhda Chirkova; Sergey Troshin

NAACL 2021long12 citations

A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code

Abstract

There is an emerging interest in the application of natural language processing models to source code processing tasks. One of the major problems in applying deep learning to software engineering is that source code often contains a lot of rare identifiers, resulting in huge vocabularies. We propose a simple, yet effective method, based on identifier anonymization, to handle out-of-vocabulary (OOV) identifiers. Our method can be treated as a preprocessing step and, therefore, allows for easy implementation. We show that the proposed OOV anonymization method significantly improves the performance of the Transformer in two code processing tasks: code completion and bug fixing.

BibTeX

@inproceedings{chirkova-troshin-2021-simple,
    title = "A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code",
    author = "Chirkova, Nadezhda  and
      Troshin, Sergey",
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-main.26/",
    doi = "10.18653/v1/2021.naacl-main.26",
    pages = "278--288"
}