Team Poznan at SemEval-2026 Task 13: Detecting Machine-Generated Code with Multiple Programming Languages, Generators, and Application Scenarios
TL;DR
Experimental results demonstrate that the GNN-based structural analysis outperforms standalone stylistic and transformer-based base-lines across all subtasks, particularly in multi-class and adversarial settings.
Abstract
Detecting machine-generated code is crucial for maintaining software security, quality and academic integrity. 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, specifically modeling source snippets as Abstract Syntax Trees (ASTs). To enhance semantic comprehension, we integrate pre-trained CodeBERT embeddings into the architecture, creating a hybrid model that incorporates structural and semantic information. We evaluate our approach on SemEval-2026 Task 13, covering binary detection, multi-class authorship attribution, and hybrid code classification. Experimental results demonstrate that our GNN-based structural analysis outperforms standalone stylistic and transformer-based base-lines across all subtasks, particularly in multi-class and adversarial settings. This work highlights the potential of GNNs for a more structural understanding of code authorship.