@inproceedings{ozaylar-2026-contestant001,
    title = "contestant001 at {S}em{E}val-2026 Task 13 Stylometric and {TF}-{IDF}-Based Detection of Machine-Generated Code",
    author = "Ozaylar, Bora",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.391/",
    pages = "3124--3129",
    ISBN = "979-8-89176-414-9",
    abstract = "Reliable detection of machine-generated codehas become increasingly important for aca-demic integrity and software quality as codegeneration is largely being undertaken by largelanguage models. This paper presents our ap-proach to SemEval-2026 Task 13, Subtask A:detecting machine-generated code in a binaryclassification setting, where we propose anensemble approach combining TF-IDF lexi-cal representations with 23 hand-crafted sty-lometric features for binary classification ofAI-generated code. Our system aims to addressthe challenge of cross-language generalizationby extracting language-agnostic patterns andcombining them with TF-IDF. While we ob-served that transformer-based models (Code-BERT, UniXcoder) noticeably underperformedunder distribution shift, our analysis revealedthat AI-generated code exhibits distinct sty-lometric patterns and our TF-IDF ensembleachieved 0.5175 Macro F1 on the task submis-sion."
}