@inproceedings{alon2019code2vec,
  title={code2vec: Learning distributed representations of code},
  author={Alon, Uri and Zilberstein, Meital and Levy, Omer and Yahav, Eran},
  booktitle={Proceedings of the ACM on Programming Languages},
  volume={3},
  number={POPL},
  pages={1--29},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@inproceedings{valdez-gomez-adorno-2025-text,
    title = "Text Graph Neural Networks for Detecting {AI}-Generated Content",
    author = "Valdez-Valenzuela, Andric  and
      G{\'o}mez-Adorno, Helena  and
      Montes-y-G{\'o}mez, Manuel",
    editor = "Alam, Firoj  and
      Nakov, Preslav  and
      Habash, Nizar  and
      Gurevych, Iryna  and
      Chowdhury, Shammur  and
      Shelmanov, Artem  and
      Wang, Yuxia  and
      Artemova, Ekaterina  and
      Kutlu, Mucahid  and
      Mikros, George",
    booktitle = "Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Conference on Computational Linguistics",
    url = "https://aclanthology.org/2025.genaidetect-1.10/",
    pages = "134--139",
    abstract = "The widespread availability of Large Language Models (LLMs) such as GPT-4 and Llama-3, among others, has led to a surge in machine-generated content across various platforms, including social media, educational tools, and academic settings. While these models demonstrate remarkable capabilities in generating coherent text, their misuse raises significant concerns. For this reason, detecting machine-generated text has become a pressing need to mitigate these risks. This research proposed a novel classification method combining text-graph representations with Graph Neural Networks (GNNs) and different node feature initialization strategies to distinguish between human-written and machine-generated content. Experimental results demonstrate that the proposed approach outperforms traditional machine learning classifiers, highlighting the effectiveness of integrating structural and semantic relationships in text."
}

@inproceedings{feng2020codebert,
  title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
  author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and Zhou, Ming},
  booktitle={Findings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2020},
  pages={Ref. paper},
  doi={10.18653/v1/2020.findings-emnlp.139}
}

@article{wu2023graph,
  title={Graph neural networks for natural language processing: A survey},
  author={Wu, Lingfei and Chen, Yu and Shen, Kai and Guo, Xiaojie and Gao, Hanning and Li, Shucheng and Pei, Jian and Long, Bo},
  journal={Foundations and Trends in Machine Learning},
  volume={16},
  number={2},
  pages={119--328},
  year={2023},
  publisher={Emerald Publishing Limited}
}

@INPROCEEDINGS {11029948,
author = { Shi, Yuling and Zhang, Hongyu and Wan, Chengcheng and Gu, Xiaodong },
booktitle = { 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE) },
title = {{ Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmers }},
year = {2025},
volume = {},
ISSN = {},
pages = {1628-1639},
abstract = { Large language models have catalyzed an unprece-dented wave in code generation. While achieving significant advances, they blur the distinctions between machine- and human-authored source code, causing integrity and authenticity issues of software artifacts. Previous methods such as DetectGPthave proven effective in discerning machine-generated texts, but they do not identify and harness the unique patterns of machine-generated code. Thus, its applicability falters when applied to code. In this paper, we carefully study the specific patterns that characterize machine- and human-authored code. Through a rigorous analysis of code attributes such as lexical diversity, conciseness, and naturalness, we expose unique patterns inherent to each source. We particularly notice that the syntactic segmentation of code is a critical factor in identifying its provenance. Based on our findings, we propose DetectCodeGPT, a novel method for detecting machine-generated code, which improves DetectGPT by capturing the distinct stylized patterns of code. Diverging from conventional techniques that depend on external LLMs for perturbations, DetectCodeGPT perturbs the code corpus by strategically inserting spaces and newlines, ensuring both efficacy and efficiency. Experiment results show that our approach significantly outperforms state-of-the-art techniques in detecting machine-generated code. 1. },
keywords = {Codes;Perturbation methods;Large language models;Source coding;Syntactics;Programming;Software;Software engineering},
doi = {10.1109/ICSE55347.2025.00005},
url = {https://doi.ieeecomputersociety.org/10.1109/ICSE55347.2025.00005},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month =May}



@INPROCEEDINGS{11315793,
  author={Endla, Purushotham and Thatipudi, Jayendra Gopal and Talluri, Madhavi Latha and Joshi, Pradeep K and Joshi, Amita and M.Anuradha},
  booktitle={2025 6th International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)}, 
  title={AutoOptiCode-LLM: An Autonomous Large Language Model Framework for Intelligent Code Generation and Optimization}, 
  year={2025},
  volume={},
  number={},
  pages={1471-1478},
  keywords={Codes;Translation;Large language models;Syntactics;Vectors;Software;Software reliability;Prompt engineering;Optimization;Software development management;Autonomous Code Generation;Large Language Models;Prompt Engineering;Code Optimization;Software Automation;Hybrid Execution Simulation},
  doi={10.1109/ICICNIS66685.2025.11315793}}

@article{DANN,
  title={Domain-adversarial training of neural networks},
  author={Ganin, Yaroslav and Ustinova, Evgeniya and Ajakan, Hana and Germain, Pascal and Larochelle, Hugo and Laviolette, Fran{\c{c}}ois and March, Mario and Lempitsky, Victor},
  journal={Journal of machine learning research},
  volume={17},
  number={59},
  pages={1--35},
  year={2016}
}

@misc{Dataset1,
      title={$\texttt{Droid}$: A Resource Suite for AI-Generated Code Detection}, 
      author={Daniil Orel and Indraneil Paul and Iryna Gurevych and Preslav Nakov},
      year={2025},
      eprint={2507.10583},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2507.10583}, 
}

@inproceedings{Dataset2,
    title = "{C}o{D}et-M4: Detecting Machine-Generated Code in Multi-Lingual, Multi-Generator and Multi-Domain Settings",
    author = "Orel, Daniil  and
      Azizov, Dilshod  and
      Nakov, Preslav",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.550/",
    pages = "10570--10593",
    ISBN = "979-8-89176-256-5",
}
@inproceedings{pan2024assessing,
  title={Assessing ai detectors in identifying ai-generated code: Implications for education},
  author={Pan, Wei Hung and Chok, Ming Jie and Wong, Jonathan Leong Shan and Shin, Yung Xin and Poon, Yeong Shian and Yang, Zhou and Chong, Chun Yong and Lo, David and Lim, Mei Kuan},
  booktitle={Proceedings of the 46th international conference on software engineering: software engineering education and training},
  pages={1--11},
  year={2024}
}
@article{team2025command,
  title={Command a: An enterprise-ready large language model},
  author={Team, Cohere and Ahmadian, A and Ahmed, M and Alammar, J and Alizadeh, M and Alnumay, Y and Althammer, S and Arkhangorodsky, A and Aryabumi, V and Aumiller, D and others},
  journal={arXiv preprint arXiv:2504.00698},
  year={2025}
}

@inproceedings{orel-etal-2026-semeval-2026,
title = "{S}em{E}val-2026 Task 13: Detecting Machine-Generated Code with Multiple Programming Languages, Generators, and Application Scenarios",
author = "Orel, Daniil and Azizov, Dilshod and Paul, Indraneil and Wang, Yuxia and Gurevych, Iryna and Nakov, Preslav",
booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
month = jun,
year = "2026",
address = "San Diego, USA",
publisher = "Association for Computational Linguistics"
}