@inproceedings{wang-etal-2026-codeflowbench,
    title = "{C}ode{F}low{B}ench: A Multi-turn, Iterative Benchmark for Complex Code Generation",
    author = "Wang, Sizhe  and
      Wang, Zhengren  and
      Ma, Dongsheng  and
      Yu, Yongan  and
      Ling, Rui  and
      li, Zhiyu  and
      Xiong, Feiyu  and
      Zhang, Wentao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.201/",
    pages = "4369--4402",
    ISBN = "979-8-89176-390-6",
    abstract = "Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components with iterative reuse of existing codes. We formalize this iterative, multi-turn paradigm as \textit{codeflow} and introduce \textbf{CodeFlowBench}, the first benchmark designed to comprehensively evaluate LLMs' ability to perform codeflow - implementing new functionality by reusing existing functions over multiple turns. CodeFlowBench comprises two complementary components: CodeFlowBench-Comp, a core collection of 5,000+ competitive programming problems from Codeforces updated via an automated pipeline and CodeFlowBench-Repo, which is sourced from GitHub repositories to better reflect real-world scenarios. Furthermore, a novel evaluation framework featured dual assessment protocol and structural metrics derived from dependency trees is introduced. Extensive experiments reveal significant performance degradation in multi-turn codeflow scenarios. Furthermore, our in-depth analysis illustrates that model performance inversely correlates with dependency complexity. These findings not only highlight the critical challenges for supporting real-world workflows, but also establish CodeFlowBench as an essential tool for advancing code generation research."
}