@inproceedings{chen-etal-2025-mastering,
    title = "Mastering the Craft of Data Synthesis for {C}ode{LLM}s",
    author = "Chen, Meng  and
      Arthur, Philip  and
      Feng, Qianyu  and
      Hoang, Cong Duy Vu  and
      Hong, Yu-Heng  and
      Moghaddam, Mahdi Kazemi  and
      Nezami, Omid  and
      Nguyen, Duc Thien  and
      Tangari, Gioacchino  and
      Vu, Duy  and
      Vu, Thanh  and
      Johnson, Mark  and
      Kenthapadi, Krishnaram  and
      Dharmasiri, Don  and
      Duong, Long  and
      Li, Yuan-Fang",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.620/",
    pages = "12484--12500",
    ISBN = "979-8-89176-189-6",
    abstract = "Large language models (LLMs) have shown impressive performance in \textit{code} understanding and generation, making coding tasks a key focus for researchers due to their practical applications and value as a testbed for LLM evaluation. Data synthesis and filtering techniques have been widely adopted and shown to be highly effective in this context. In this paper, we present a focused survey and taxonomy of these techniques, emphasizing recent advancements. We highlight key challenges, explore future research directions, and offer practical guidance for new researchers entering the field."
}