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        <title>contestant001 at SemEval-2026 Task 13 Stylometric and TF-IDF-Based Detection of Machine-Generated Code</title>
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        <namePart type="given">Bora</namePart>
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            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
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            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
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            <namePart type="given">Kai</namePart>
            <namePart type="family">North</namePart>
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            <namePart type="given">Mamoru</namePart>
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    <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.</abstract>
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        <url>https://aclanthology.org/2026.semeval-1.391/</url>
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%0 Conference Proceedings
%T contestant001 at SemEval-2026 Task 13 Stylometric and TF-IDF-Based Detection of Machine-Generated Code
%A Ozaylar, Bora
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F ozaylar-2026-contestant001
%X 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.
%U https://aclanthology.org/2026.semeval-1.391/
%P 3124-3129

Markdown (Informal)

[contestant001 at SemEval-2026 Task 13 Stylometric and TF-IDF-Based Detection of Machine-Generated Code](https://aclanthology.org/2026.semeval-1.391/) (Ozaylar, SemEval 2026)

• contestant001 at SemEval-2026 Task 13 Stylometric and TF-IDF-Based Detection of Machine-Generated Code (Ozaylar, SemEval 2026)

ACL

• Bora Ozaylar. 2026. contestant001 at SemEval-2026 Task 13 Stylometric and TF-IDF-Based Detection of Machine-Generated Code. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3124–3129, San Diego, California, USA. Association for Computational Linguistics.