@inproceedings{peng-etal-2026-repogenesis,
    title = "{R}epo{G}enesis: Benchmarking End-to-End Microservice Generation from Readme to Repository",
    author = "Peng, Zhiyuan  and
      Yin, Xin  and
      Zhao, Pu  and
      Yang, Fangkai  and
      Wang, Lu  and
      Jia, Ran  and
      Chen, Xu  and
      Lin, Qingwei  and
      Rajmohan, Saravan  and
      Zhang, Dongmei",
    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.415/",
    pages = "9161--9189",
    ISBN = "979-8-89176-390-6",
    abstract = "Large language models and agents have achieved remarkable progress in code generation. However, existing benchmarks focus on isolated function/class-level generation (e.g., ClassEval) or modifications to existing codebases (e.g., SWE-Bench), neglecting complete microservice repository generation that reflects real-world 0-to-1 development workflows. To bridge this gap, we introduce RepoGenesis, the first multilingual benchmark for repository-level end-to-end web microservice generation, comprising 106 repositories (60 Python, 46 Java) across 18 domains and 11 frameworks, with 1,258 API endpoints and 2,335 test cases verified through a ``review-rebuttal'' quality assurance process. We evaluate open-source agents (e.g., DeepCode) and commercial IDEs (e.g., Cursor) using Pass@1, API Coverage (AC), and Deployment Success Rate (DSR). Results reveal that despite high AC (up to 73.91{\%}) and DSR (up to 100{\%}), the best-performing system achieves only 23.67{\%} Pass@1 on Python and 21.45{\%} on Java, exposing deficiencies in architectural coherence, dependency management, and cross-file consistency. Notably, RepoGenesis-8B, fine-tuned on RepoGenesis (train), achieves performance comparable to GPT-5 mini, demonstrating the quality of RepoGenesis for advancing microservice generation. We release our benchmark at \url{https://github.com/pzy2000/RepoGenesis}."
}