LAFED at SemEval-2026 Task 13: Language-Agnostic Feature Engineering for Cross-Lingual AI-Generated Code Detection
Year: 2026
Type: conference-paper
Abstract: Robust detection of AI-generated source code across programming languages remains challenging due to language-specific cues and traintest distribution shifts.We present LAFED (Language-Agnostic Feature Engineering Detector), a feature-engineering approach trained on {Python, Java, C++} and evaluated on a multilingual test set that includes unseen languages {C, C#, Go, JavaScript, PHP}.LAFED combines (i) structural skeletal features (indentation, control-flow density, and approximations of McCabe/Halstead complexity), (ii) character and whitespace statistics inspired by stylometry, and (iii) micro-style patterns (operator spacing, blank lines, indentation consistency).Using XGBoost (Chen and Guestrin, 2016) with Optuna hyperparameter search (Akiba et al., 2019), our best model achieves macro-F1=0.7570 on the organizer-provided 1,000-sample test set for reproducible experiments, while the official hidden evaluation yields macro-F1=0.75209(5th place in Subtask A).Per-language analysis shows strong transfer to C# (0.7753) and JavaScript (0.7683), but weaker performance on Go (0.6400) and PHP (0.5238).
Author: Juan Villate Lemus
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166888646