FMISUYotkovaKastreva at SemEval-2026 Task 13: Lightweight Detection of LLM-Generated Code via Stylometric Signals
Year: 2026
Type: conference-paper
Abstract: SemEval-2026 Task 13 investigates machinegenerated code detection across multiple programming languages and application scenarios, asking participating systems to generalize to unseen languages and domains.This paper describes our participation in Subtask A (binary classification) and explores both pretrained code encoders and lightweight feature-based methods.We design ratio-based features that are less sensitive to snippet length.To support the extraction of descriptivenessrelated signals, we use parsing engines and a programming-language classifier.Additionally, we train a separate code-vs-text line classifier to identify raw natural language segments embedded within samples.We combine a shallow decision tree with heuristic rules derived from data analysis to produce the final predictions.Our approach is computationally efficient, requires only CPU resources for training, and achieves near-instant inference time, offering a lightweight alternative to large pretrained models.Our code is available at https://github.com/violeta-kastreva/ SemEvalTask13-SubtaskA.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166844782