@inproceedings{nitin-etal-2021-direct,
    title = "{DIRECT} : A Transformer-based Model for Decompiled Identifier Renaming",
    author = "Nitin, Vikram  and
      Saieva, Anthony  and
      Ray, Baishakhi  and
      Kaiser, Gail",
    editor = "Lachmy, Royi  and
      Yao, Ziyu  and
      Durrett, Greg  and
      Gligoric, Milos  and
      Li, Junyi Jessy  and
      Mooney, Ray  and
      Neubig, Graham  and
      Su, Yu  and
      Sun, Huan  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.nlp4prog-1.6/",
    doi = "10.18653/v1/2021.nlp4prog-1.6",
    pages = "48--57",
    abstract = "Decompiling binary executables to high-level code is an important step in reverse engineering scenarios, such as malware analysis and legacy code maintenance. However, the generated high-level code is difficult to understand since the original variable names are lost. In this paper, we leverage transformer models to reconstruct the original variable names from decompiled code. Inherent differences between code and natural language present certain challenges in applying conventional transformer-based architectures to variable name recovery. We propose DIRECT, a novel transformer-based architecture customized specifically for the task at hand. We evaluate our model on a dataset of decompiled functions and find that DIRECT outperforms the previous state-of-the-art model by up to 20{\%}. We also present ablation studies evaluating the impact of each of our modifications. We make the source code of DIRECT available to encourage reproducible research."
}