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AICD Bench: A Challenging Benchmark for AI-Generated Code Detection

Paper · 2026 · Daniil Orel, Dilshod Azizov, Indraneil Paul et al.

Summary

Tools for detecting machine-written source code are usually tested on narrow, custom datasets. These cover few languages and generators and mostly test human-vs-AI separation within the same distribution, which tells little about how detectors behave in real use. The authors address this with AICD Bench, a unified benchmark of about 2M code samples. It covers 77 generators from 11 model families, including recent reasoning models, and 9 programming languages. It extends DroidCollection with PHP and Rust, new LLMs and extra generation sources, and it comes with standard splits, protocols and evaluation scripts.

The benchmark defines three tasks of growing difficulty:

  • binary detection under language and domain shift;
  • attributing code to a model family rather than to a single model;
  • a multi-class setting separating human, machine, hybrid and adversarial code.

Both classical and neural detectors fall well short of practical reliability, especially under distribution shift and on hybrid or adversarial samples. On the robust binary task, a plain SVM outperforms the deep models but still scores below random guessing. The resource shifts evaluation toward realistic deployment conditions.

Key points

  • Scale: ~2M samples, 77 LLMs in 11 families, and 9 languages: Python, Rust, Java and JavaScript, PHP, Go, plus the C family (C, C++ and C#). Data and code are released at huggingface.co/AICD-bench.
  • Built on DroidCollection (7 languages, 43 LLMs, ~1M samples), which was extended using the original Droid generation protocol.
  • About 500K new samples came from inverse-instruction prompting over StarCoderData and CodeNet. Further samples were generated from docstrings and comments in The Vault.
  • CodeMirage (210K samples, 10 languages) is the closest prior benchmark, but it handles only binary detection and appears near saturation, with fine-tuned CodeT5+ above 80% F1.
  • Planned next steps include a private hidden-label test split, broader language coverage, and continuous addition of newly released LLMs. Adversarial prompts are withheld to limit misuse.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Evaluation & benchmarks · 1,220 works

Cite

Cite as Daniil Orel, Dilshod Azizov, Indraneil Paul, Yuxia Wang, Iryna Gurevych and Preslav Nakov. 2026. AICD Bench: A Challenging Benchmark for AI-Generated Code Detection. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.eacl-long.325

@inproceedings{orel2026aicd,
  title = {{AICD Bench: A Challenging Benchmark for AI-Generated Code Detection}},
  author = {Daniil Orel, Dilshod Azizov, Indraneil Paul, Yuxia Wang, Iryna Gurevych and Preslav Nakov},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.325},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.325}
}

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