% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

% bibtex either from https://aclanthology.org/ or https://dblp.org/
% formatting: {first author lastname}-{publication year}-{first word of title}

@inproceedings{Akiba-2019-Optuna,
  author       = {Takuya Akiba and
                  Shotaro Sano and
                  Toshihiko Yanase and
                  Takeru Ohta and
                  Masanori Koyama},
  editor       = {Ankur Teredesai and
                  Vipin Kumar and
                  Ying Li and
                  R{\'{o}}mer Rosales and
                  Evimaria Terzi and
                  George Karypis},
  title        = {Optuna: {A} Next-generation Hyperparameter Optimization Framework},
  booktitle    = {Proceedings of the 25th {ACM} {SIGKDD} International Conference on
                  Knowledge Discovery {\&} Data Mining, {KDD} 2019, Anchorage, AK,
                  USA, August 4-8, 2019},
  pages        = {2623--2631},
  publisher    = {{ACM}},
  year         = {2019},
  url          = {https://doi.org/10.1145/3292500.3330701},
  doi          = {10.1145/3292500.3330701},
  timestamp    = {Sun, 02 Nov 2025 21:27:16 +0100},
  biburl       = {https://dblp.org/rec/conf/kdd/AkibaSYOK19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{orel-etal-2025-droid,
    title = "$\texttt{Droid}$: A Resource Suite for {AI}-Generated Code Detection",
    author = "Orel, Daniil  and
      Paul, Indraneil  and
      Gurevych, Iryna  and
      Nakov, Preslav",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.1593/",
    doi = "10.18653/v1/2025.emnlp-main.1593",
    pages = "31263--31289",
    ISBN = "979-8-89176-332-6",
    abstract = "We present DroidCollection, the most extensive open data suite for training and evaluating machine-generated code detectors, comprising over a million code samples, seven programming languages, outputs from 43 coding models, and three real-world coding domains. Alongside fully AI-generated examples, our collection includes human-AI co-authored code, as well as adversarial examples explicitly crafted to evade detection. Subsequently, we develop DroidDetect, a suite of encoder-only detectors trained using a multi-task objective over DroidCollection. Our experiments show that existing detectors' performance fails to generalise to diverse coding domains and programming languages outside of their narrow training data. We further demonstrate that while most detectors are easily compromised by humanising the output distributions using superficial prompting and alignment approaches, this problem can be easily amended by training on a small number of adversarial examples. Finally, we demonstrate the effectiveness of metric learning and uncertainty-based resampling as way to enhance detector training on possibly noisy distributions."
}

@inproceedings{warner-etal-2025-smarter,
    title = "Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference",
    author = {Warner, Benjamin  and
      Chaffin, Antoine  and
      Clavi{\'e}, Benjamin  and
      Weller, Orion  and
      Hallstr{\"o}m, Oskar  and
      Taghadouini, Said  and
      Gallagher, Alexis  and
      Biswas, Raja  and
      Ladhak, Faisal  and
      Aarsen, Tom  and
      Adams, Griffin Thomas  and
      Howard, Jeremy  and
      Poli, Iacopo},
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.127/",
    doi = "10.18653/v1/2025.acl-long.127",
    pages = "2526--2547",
    ISBN = "979-8-89176-251-0",
    abstract = "Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs."
}

@inproceedings{devlin-etal-2019-bert,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1423/",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
    abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
}

@article{Maaten-2008-Viusalizing,
  author  = {Laurens van der Maaten and Geoffrey Hinton},
  title   = {Visualizing Data using t-SNE},
  journal = {Journal of Machine Learning Research},
  year    = {2008},
  volume  = {9},
  number  = {86},
  pages   = {2579--2605},
  url     = {http://jmlr.org/papers/v9/vandermaaten08a.html}
}

@article{pearson1901liii,
  title={LIII. On lines and planes of closest fit to systems of points in space},
  author={Pearson, Karl},
  journal={The London, Edinburgh, and Dublin philosophical magazine and journal of science},
  volume={2},
  number={11},
  pages={559--572},
  year={1901},
  publisher={Taylor \& Francis}
}
@article{Nguyen-2024-GPTSniffer,
  author       = {Phuong T. Nguyen and
                  Juri Di Rocco and
                  Claudio Di Sipio and
                  Riccardo Rubei and
                  Davide Di Ruscio and
                  Massimiliano Di Penta},
  title        = {GPTSniffer: {A} CodeBERT-based classifier to detect source code written
                  by ChatGPT},
  journal      = {J. Syst. Softw.},
  volume       = {214},
  pages        = {112059},
  year         = {2024},
  url          = {https://doi.org/10.1016/j.jss.2024.112059},
  doi          = {10.1016/J.JSS.2024.112059},
  timestamp    = {Fri, 19 Jul 2024 23:18:06 +0200},
  biburl       = {https://dblp.org/rec/journals/jss/NguyenRSRRP24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{feng-etal-2020-codebert,
    title = "{C}ode{BERT}: 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",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.findings-emnlp.139/",
    doi = "10.18653/v1/2020.findings-emnlp.139",
    pages = "1536--1547",
    abstract = "We present CodeBERT, a bimodal pre-trained model for programming language (PL) and natural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language code search, code documentation generation, etc. We develop CodeBERT with Transformer-based neural architecture, and train it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators. This enables us to utilize both ``bimodal'' data of NL-PL pairs and ``unimodal data, where the former provides input tokens for model training while the latter helps to learn better generators. We evaluate CodeBERT on two NL-PL applications by fine-tuning model parameters. Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation. Furthermore, to investigate what type of knowledge is learned in CodeBERT, we construct a dataset for NL-PL probing, and evaluate in a zero-shot setting where parameters of pre-trained models are fixed. Results show that CodeBERT performs better than previous pre-trained models on NLPL probing."
}

@article{Husain-2019-CodeSearchNet,
  author       = {Hamel Husain and
                  Ho{-}Hsiang Wu and
                  Tiferet Gazit and
                  Miltiadis Allamanis and
                  Marc Brockschmidt},
  title        = {CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
  journal      = {CoRR},
  volume       = {abs/1909.09436},
  year         = {2019},
  url          = {http://arxiv.org/abs/1909.09436},
  eprinttype    = {arXiv},
  eprint       = {1909.09436},
  timestamp    = {Tue, 24 Sep 2019 11:33:51 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1909-09436.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{Lui-2019-RoBERTa,
  author       = {Yinhan Liu and
                  Myle Ott and
                  Naman Goyal and
                  Jingfei Du and
                  Mandar Joshi and
                  Danqi Chen and
                  Omer Levy and
                  Mike Lewis and
                  Luke Zettlemoyer and
                  Veselin Stoyanov},
  title        = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
  journal      = {CoRR},
  volume       = {abs/1907.11692},
  year         = {2019},
  url          = {http://arxiv.org/abs/1907.11692},
  eprinttype    = {arXiv},
  eprint       = {1907.11692},
  timestamp    = {Tue, 11 Feb 2025 12:43:01 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{orel-etal-2025-codet,
    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/",
    doi = "10.18653/v1/2025.findings-acl.550",
    pages = "10570--10593",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) have revolutionized code generation, automating programming with remarkable efficiency. However, this has had important consequences for programming skills, ethics, and assessment integrity, thus making the detection of LLM-generated code essential for maintaining accountability and standards. While, there has been some previous research on this problem, it generally lacks domain coverage and robustness, and only covers a small number of programming languages. Here, we aim to bridge this gap. In particular, we propose a framework capable of distinguishing between human-written and LLM-generated program code across multiple programming languages, code generators, and domains. We use a large-scale dataset from renowned platforms and LLM-based code generators, alongside applying rigorous data quality checks, feature engineering, and comparative analysis of traditional machine learning models, pre-trained language models (PLMs), and LLMs for code detection. We perform an evaluation on out-of-domain scenarios, such as detecting authorship and hybrid authorship of generated code and generalizing to unseen models, domains, and programming languages. Our extensive experiments show that our framework effectively distinguishes human-written from LLM-generated program code, setting a new benchmark for the task."
}

@inproceedings{wang-etal-2021-codet5,
    title = "{C}ode{T}5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation",
    author = "Wang, Yue  and
      Wang, Weishi  and
      Joty, Shafiq  and
      Hoi, Steven C.H.",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.685/",
    doi = "10.18653/v1/2021.emnlp-main.685",
    pages = "8696--8708",
    abstract = "Pre-trained models for Natural Languages (NL) like BERT and GPT have been recently shown to transfer well to Programming Languages (PL) and largely benefit a broad set of code-related tasks. Despite their success, most current methods either rely on an encoder-only (or decoder-only) pre-training that is suboptimal for generation (resp. understanding) tasks or process the code snippet in the same way as NL, neglecting the special characteristics of PL such as token types. We present CodeT5, a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers. Our model employs a unified framework to seamlessly support both code understanding and generation tasks and allows for multi-task learning. Besides, we propose a novel identifier-aware pre-training task that enables the model to distinguish which code tokens are identifiers and to recover them when they are masked. Furthermore, we propose to exploit the user-written code comments with a bimodal dual generation task for better NL-PL alignment. Comprehensive experiments show that CodeT5 significantly outperforms prior methods on understanding tasks such as code defect detection and clone detection, and generation tasks across various directions including PL-NL, NL-PL, and PL-PL. Further analysis reveals that our model can better capture semantic information from code. Our code and pre-trained models are released at \url{https://github.com/salesforce/CodeT5}."
}

@article{lin-2020-focal,
  author       = {Tsung{-}Yi Lin and
                  Priya Goyal and
                  Ross B. Girshick and
                  Kaiming He and
                  Piotr Doll{\'{a}}r},
  title        = {Focal Loss for Dense Object Detection},
  journal      = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
  volume       = {42},
  number       = {2},
  pages        = {318--327},
  year         = {2020},
  url          = {https://doi.org/10.1109/TPAMI.2018.2858826},
  doi          = {10.1109/TPAMI.2018.2858826},
  timestamp    = {Sat, 30 May 2020 20:02:13 +0200},
  biburl       = {https://dblp.org/rec/journals/pami/LinGGHD20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Cui-2019-Class,
  author       = {Yin Cui and
                  Menglin Jia and
                  Tsung{-}Yi Lin and
                  Yang Song and
                  Serge J. Belongie},
  title        = {Class-Balanced Loss Based on Effective Number of Samples},
  booktitle    = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
                  2019, Long Beach, CA, USA, June 16-20, 2019},
  pages        = {9268--9277},
  publisher    = {Computer Vision Foundation / {IEEE}},
  year         = {2019},
  url          = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Cui\_Class-Balanced\_Loss\_Based\_on\_Effective\_Number\_of\_Samples\_CVPR\_2019\_paper.html},
  doi          = {10.1109/CVPR.2019.00949},
  timestamp    = {Thu, 23 Jun 2022 19:54:31 +0200},
  biburl       = {https://dblp.org/rec/conf/cvpr/CuiJLSB19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{Kumar-2024-Large,
  author       = {Pranjal Kumar},
  title        = {Large language models (LLMs): survey, technical frameworks, and future
                  challenges},
  journal      = {Artif. Intell. Rev.},
  volume       = {57},
  number       = {9},
  pages        = {260},
  year         = {2024},
  url          = {https://doi.org/10.1007/s10462-024-10888-y},
  doi          = {10.1007/S10462-024-10888-Y},
  timestamp    = {Tue, 01 Apr 2025 18:59:21 +0200},
  biburl       = {https://dblp.org/rec/journals/air/Kumar24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{HAQUE2025100204,
title = {LLMs: A game-changer for software engineers?},
journal = {BenchCouncil Transactions on Benchmarks, Standards and Evaluations},
volume = {5},
number = {1},
pages = {100204},
year = {2025},
issn = {2772-4859},
doi = {https://doi.org/10.1016/j.tbench.2025.100204},
url = {https://www.sciencedirect.com/science/article/pii/S2772485925000171},
author = {Md. Asraful Haque},
keywords = {Software engineering, Large language model, AI tools, Coding, Testing, Debugging},
abstract = {Large Language Models (LLMs) like GPT-3 and GPT-4 have emerged as groundbreaking innovations with capabilities that extend far beyond traditional AI applications. These sophisticated models, trained on massive datasets, can generate human-like text, respond to complex queries, and even write and interpret code. Their potential to revolutionize software development has captivated the software engineering (SE) community, sparking debates about their transformative impact. Through a critical analysis of technical strengths, limitations, real-world case studies, and future research directions, this paper argues that LLMs are not just reshaping how software is developed but are redefining the role of developers. While challenges persist, LLMs offer unprecedented opportunities for innovation and collaboration. Early adoption of LLMs in software engineering is crucial to stay competitive in this rapidly evolving landscape. This paper serves as a guide, helping developers, organizations, and researchers understand how to harness the power of LLMs to streamline workflows and acquire the necessary skills.}
}

@inproceedings{Kojima-2022-Large,
  author       = {Takeshi Kojima and
                  Shixiang Shane Gu and
                  Machel Reid and
                  Yutaka Matsuo and
                  Yusuke Iwasawa},
  editor       = {Sanmi Koyejo and
                  S. Mohamed and
                  A. Agarwal and
                  Danielle Belgrave and
                  K. Cho and
                  A. Oh},
  title        = {Large Language Models are Zero-Shot Reasoners},
  booktitle    = {Advances in Neural Information Processing Systems 35: Annual Conference
                  on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans,
                  LA, USA, November 28 - December 9, 2022},
  year         = {2022},
  url          = {http://papers.nips.cc/paper\_files/paper/2022/hash/8bb0d291acd4acf06ef112099c16f326-Abstract-Conference.html},
  timestamp    = {Mon, 08 Jan 2024 16:31:34 +0100},
  biburl       = {https://dblp.org/rec/conf/nips/KojimaGRMI22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{Idialu-2024-Whodunit,
  author       = {Oseremen Joy Idialu and
                  Noble Saji Mathews and
                  Rungroj Maipradit and
                  Joanne M. Atlee and
                  Meiyappan Nagappan},
  editor       = {Diomidis Spinellis and
                  Alberto Bacchelli and
                  Eleni Constantinou},
  title        = {Whodunit: Classifying Code as Human Authored or {GPT-4} generated-
                  {A} case study on CodeChef problems},
  booktitle    = {21st {IEEE/ACM} International Conference on Mining Software Repositories,
                  {MSR} 2024, Lisbon, Portugal, April 15-16, 2024},
  pages        = {394--406},
  publisher    = {{ACM}},
  year         = {2024},
  url          = {https://doi.org/10.1145/3643991.3644926},
  doi          = {10.1145/3643991.3644926},
  timestamp    = {Sun, 19 Jan 2025 13:31:26 +0100},
  biburl       = {https://dblp.org/rec/conf/msr/IdialuMMAN24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{Li-2023-Discriminating,
  author       = {Ke Li and
                  Sheng Hong and
                  Cai Fu and
                  Yunhe Zhang and
                  Ming Liu},
  title        = {Discriminating Human-authored from ChatGPT-Generated Code Via Discernable
                  Feature Analysis},
  booktitle    = {34th {IEEE} International Symposium on Software Reliability Engineering,
                  {ISSRE} 2023 - Workshops, Florence, Italy, October 9-12, 2023},
  pages        = {120--127},
  publisher    = {{IEEE}},
  year         = {2023},
  url          = {https://doi.org/10.1109/ISSREW60843.2023.00059},
  doi          = {10.1109/ISSREW60843.2023.00059},
  timestamp    = {Wed, 12 Feb 2025 13:50:58 +0100},
  biburl       = {https://dblp.org/rec/conf/issre/LiHFZL23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{Xu-2025-Distinguishing,
  author       = {Xiaodan Xu and
                  Chao Ni and
                  Xinrong Guo and
                  Shaoxuan Liu and
                  Xiaoya Wang and
                  Kui Liu and
                  Xiaohu Yang},
  title        = {Distinguishing LLM-Generated from Human-Written Code by Contrastive
                  Learning},
  journal      = {{ACM} Trans. Softw. Eng. Methodol.},
  volume       = {34},
  number       = {4},
  pages        = {91:1--91:31},
  year         = {2025},
  url          = {https://doi.org/10.1145/3705300},
  doi          = {10.1145/3705300},
  timestamp    = {Sat, 06 Sep 2025 20:29:43 +0200},
  biburl       = {https://dblp.org/rec/journals/tosem/XuNGLWLY25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{guo-etal-2022-unixcoder,
    title = "{U}ni{X}coder: Unified Cross-Modal Pre-training for Code Representation",
    author = "Guo, Daya  and
      Lu, Shuai  and
      Duan, Nan  and
      Wang, Yanlin  and
      Zhou, Ming  and
      Yin, Jian",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.499/",
    doi = "10.18653/v1/2022.acl-long.499",
    pages = "7212--7225",
    abstract = "Pre-trained models for programming languages have recently demonstrated great success on code intelligence. To support both code-related understanding and generation tasks, recent works attempt to pre-train unified encoder-decoder models. However, such encoder-decoder framework is sub-optimal for auto-regressive tasks, especially code completion that requires a decoder-only manner for efficient inference. In this paper, we present UniXcoder, a unified cross-modal pre-trained model for programming language. The model utilizes mask attention matrices with prefix adapters to control the behavior of the model and leverages cross-modal contents like AST and code comment to enhance code representation. To encode AST that is represented as a tree in parallel, we propose a one-to-one mapping method to transform AST in a sequence structure that retains all structural information from the tree. Furthermore, we propose to utilize multi-modal contents to learn representation of code fragment with contrastive learning, and then align representations among programming languages using a cross-modal generation task. We evaluate UniXcoder on five code-related tasks over nine datasets. To further evaluate the performance of code fragment representation, we also construct a dataset for a new task, called zero-shot code-to-code search. Results show that our model achieves state-of-the-art performance on most tasks and analysis reveals that comment and AST can both enhance UniXcoder."
}
@inproceedings{nipf-2017-Semi,
  author       = {Thomas N. Kipf and
                  Max Welling},
  title        = {Semi-Supervised Classification with Graph Convolutional Networks},
  booktitle    = {5th International Conference on Learning Representations, {ICLR} 2017,
                  Toulon, France, April 24-26, 2017, Conference Track Proceedings},
  publisher    = {OpenReview.net},
  year         = {2017},
  url          = {https://openreview.net/forum?id=SJU4ayYgl},
  timestamp    = {Thu, 25 Jul 2019 14:25:55 +0200},
  biburl       = {https://dblp.org/rec/conf/iclr/KipfW17.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{Ostroumova-2018-CatBoost,
  author       = {Liudmila Ostroumova Prokhorenkova and
                  Gleb Gusev and
                  Aleksandr Vorobev and
                  Anna Veronika Dorogush and
                  Andrey Gulin},
  editor       = {Samy Bengio and
                  Hanna M. Wallach and
                  Hugo Larochelle and
                  Kristen Grauman and
                  Nicol{\`{o}} Cesa{-}Bianchi and
                  Roman Garnett},
  title        = {CatBoost: unbiased boosting with categorical features},
  booktitle    = {Advances in Neural Information Processing Systems 31: Annual Conference
                  on Neural Information Processing Systems 2018, NeurIPS 2018, December
                  3-8, 2018, Montr{\'{e}}al, Canada},
  pages        = {6639--6649},
  year         = {2018},
  url          = {https://proceedings.neurips.cc/paper/2018/hash/14491b756b3a51daac41c24863285549-Abstract.html},
  timestamp    = {Mon, 16 May 2022 15:41:51 +0200},
  biburl       = {https://dblp.org/rec/conf/nips/ProkhorenkovaGV18.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@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"
}

@inproceedings{orel2026aicdbenchchallengingbenchmark,
    title = "{AICD} Bench: A Challenging Benchmark for AI-Generated Code Detection",
    author = "Daniil Orel and Dilshod Azizov and Indraneil Paul and Yuxia Wang and Iryna Gurevych and Preslav Nakov",
    year = "2026",
    booktitle = "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = mar,
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2602.02079",
}