@inproceedings{j-etal-2026-ssn,
    title = "{SSN}-{CSE}-{CODECATALYSTS} at {S}em{E}val-2026 Task 13: Integrating Transformer Semantics and {AST}-Derived Structural Features for {AI}-Generated Code Detection.",
    author = "J, Bhuvana  and
      Mahendran, Ramanan  and
      S, Siddharth Chandrasekar  and
      J, Pragatheesh  and
      P, Rethanya",
    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://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.141/",
    pages = "1027--1031",
    ISBN = "979-8-89176-414-9",
    abstract = "Pre-trained transformers often struggle with multi-lingual code classification due to sequence length constraints and difficulties in explicitly capturing deep structural complexities. To address this for SemEval Task 13, a hybrid neural architecture that fuses CodeBERT{'}s semantic embeddings is proposed. Handcrafted software engineering metrics is presented, with a Head+Tail truncation strategy to preserve crucial logic in long sequences while simultaneously extracting explicit Abstract Syntax Tree (AST) features via tree-sitter{---}including maximum depth, branching factor, and cyclomatic complexity. By integrating dense language model representations with explicit structural heuristics, this work provides a robust and scalable solution for enhanced code classification."
}