@inproceedings{siera-etal-2026-team,
title = "Team Poznan at {S}em{E}val-2026 Task 13: Detecting Machine-Generated Code with Multiple Programming Languages, Generators, and Application Scenarios",
author = "Siera, Dawid and
Kaczmarek, Anatol and
Kamzela, Wiktor and
Dobosz, Adam and
Dutkiewicz, Jakub",
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://aclanthology.org/2026.semeval-1.288/",
pages = "2275--2280",
ISBN = "979-8-89176-414-9",
abstract = "Detecting machine-generated code is crucial for maintaining software security and quality. Traditional approaches often rely on stylistic or statistical features, which are increasingly circumvented by advanced code generation models. This paper introduces a novel approach leveraging Graph Neural Networks (GNNs) to capture the structural characteristics of code, representing it as a program dependency graph. We demonstrate that our GNN-based classifier outperforms both traditional and embedding based methods on benchmark datasets, achieving improved accuracy and robustness in identifying code produced by various generation techniques. This work highlights the potential of GNNs for a more structural understanding of code authorship."
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%0 Conference Proceedings
%T Team Poznan at SemEval-2026 Task 13: Detecting Machine-Generated Code with Multiple Programming Languages, Generators, and Application Scenarios
%A Siera, Dawid
%A Kaczmarek, Anatol
%A Kamzela, Wiktor
%A Dobosz, Adam
%A Dutkiewicz, Jakub
%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 siera-etal-2026-team
%X Detecting machine-generated code is crucial for maintaining software security and quality. Traditional approaches often rely on stylistic or statistical features, which are increasingly circumvented by advanced code generation models. This paper introduces a novel approach leveraging Graph Neural Networks (GNNs) to capture the structural characteristics of code, representing it as a program dependency graph. We demonstrate that our GNN-based classifier outperforms both traditional and embedding based methods on benchmark datasets, achieving improved accuracy and robustness in identifying code produced by various generation techniques. This work highlights the potential of GNNs for a more structural understanding of code authorship.
%U https://aclanthology.org/2026.semeval-1.288/
%P 2275-2280
Markdown (Informal)
[Team Poznan at SemEval-2026 Task 13: Detecting Machine-Generated Code with Multiple Programming Languages, Generators, and Application Scenarios](https://aclanthology.org/2026.semeval-1.288/) (Siera et al., SemEval 2026)
ACL
- Dawid Siera, Anatol Kaczmarek, Wiktor Kamzela, Adam Dobosz, and Jakub Dutkiewicz. 2026. Team Poznan at SemEval-2026 Task 13: Detecting Machine-Generated Code with Multiple Programming Languages, Generators, and Application Scenarios. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2275–2280, San Diego, California, USA. Association for Computational Linguistics.