Work

Stylometry at SemEval-2026 Task 13: Clustered Stylometric Modeling for Machine-Generated Code Detection

Year: 2026

Type: conference-paper

Abstract: Machine-generated code detection is examined under out-of-distribution conditions where robust generalization is required.A hybrid feature representation is used in which code snippets are encoded through character-level TF-IDF patterns together with explicit structural indicators capturing properties such as verbosity and formatting behavior.Variability across generators is handled through clusteringbased expert specialization, and predictions are produced using an ensemble of logistic regression and Naïve Bayes models with calibrated thresholds.Experimental results show that the proposed approach achieves a macro F1-score of 0.6125 on the official test set, performing competitively despite relying on simple linear classifiers.The findings suggest that persistent structural patterns in code provide reliable cross-domain signals for identifying machinegenerated programs.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/2026.semeval-1.172

Authors: Sruthi Santhanam, Parthib Sarkar, Yashvardhan Sharma

Institution: Birla Institute of Technology and Science, Pilani

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Birla Institute of Technology and Science, Pilani

OpenAlex ID: W7166876551