@inproceedings{gorantla-etal-2026-teamsls,
title = "{T}eam{SLS} at {S}em{E}val-2026 Task 13: Detecting Machine-Generated Code with {C}ode{BERT} and Structural Features",
author = "Gorantla, Sai Laasya and
Naveen, Shreemithra and
Bethard, Steven",
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-1.318/",
pages = "2520--2526",
ISBN = "979-8-89176-414-9",
abstract = "We describe our system for SemEval-2026 Task 13 Subtask A, which focuses on detecting whether source code is written by a human or generated by an AI system. We propose a hybrid approach that combines semantic embeddings from CodeBERT with lightweight, language-agnostic structural features extracted using Tree-sitter. We compute normalized structural ratios such as nesting depth, logic density, complexity per line, average line length, and punctuation frequency. These structural signals are concatenated with CodeBERT embeddings and passed to a linear classifier for binary prediction. Experimental results on the official validation split show that combining semantic and normalized structural representations substantially improves the model{'}s detection performance on seen-language distributions. However, results on unseen test data reveal significant performance degradation under cross-language distribution shifts. On the official leaderboard, our system ranked 47th out of 81 participating teams."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gorantla-etal-2026-teamsls">
<titleInfo>
<title>TeamSLS at SemEval-2026 Task 13: Detecting Machine-Generated Code with CodeBERT and Structural Features</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sai</namePart>
<namePart type="given">Laasya</namePart>
<namePart type="family">Gorantla</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shreemithra</namePart>
<namePart type="family">Naveen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Steven</namePart>
<namePart type="family">Bethard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Kochmar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Debanjan</namePart>
<namePart type="family">Ghosh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kai</namePart>
<namePart type="family">North</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mamoru</namePart>
<namePart type="family">Komachi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-414-9</identifier>
</relatedItem>
<abstract>We describe our system for SemEval-2026 Task 13 Subtask A, which focuses on detecting whether source code is written by a human or generated by an AI system. We propose a hybrid approach that combines semantic embeddings from CodeBERT with lightweight, language-agnostic structural features extracted using Tree-sitter. We compute normalized structural ratios such as nesting depth, logic density, complexity per line, average line length, and punctuation frequency. These structural signals are concatenated with CodeBERT embeddings and passed to a linear classifier for binary prediction. Experimental results on the official validation split show that combining semantic and normalized structural representations substantially improves the model’s detection performance on seen-language distributions. However, results on unseen test data reveal significant performance degradation under cross-language distribution shifts. On the official leaderboard, our system ranked 47th out of 81 participating teams.</abstract>
<identifier type="citekey">gorantla-etal-2026-teamsls</identifier>
<location>
<url>https://aclanthology.org/2026.semeval-1.318/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>2520</start>
<end>2526</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T TeamSLS at SemEval-2026 Task 13: Detecting Machine-Generated Code with CodeBERT and Structural Features
%A Gorantla, Sai Laasya
%A Naveen, Shreemithra
%A Bethard, Steven
%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 gorantla-etal-2026-teamsls
%X We describe our system for SemEval-2026 Task 13 Subtask A, which focuses on detecting whether source code is written by a human or generated by an AI system. We propose a hybrid approach that combines semantic embeddings from CodeBERT with lightweight, language-agnostic structural features extracted using Tree-sitter. We compute normalized structural ratios such as nesting depth, logic density, complexity per line, average line length, and punctuation frequency. These structural signals are concatenated with CodeBERT embeddings and passed to a linear classifier for binary prediction. Experimental results on the official validation split show that combining semantic and normalized structural representations substantially improves the model’s detection performance on seen-language distributions. However, results on unseen test data reveal significant performance degradation under cross-language distribution shifts. On the official leaderboard, our system ranked 47th out of 81 participating teams.
%U https://aclanthology.org/2026.semeval-1.318/
%P 2520-2526
Markdown (Informal)
[TeamSLS at SemEval-2026 Task 13: Detecting Machine-Generated Code with CodeBERT and Structural Features](https://aclanthology.org/2026.semeval-1.318/) (Gorantla et al., SemEval 2026)
ACL