@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://aclanthology.org/2026.acl-long.415/",
doi = "10.18653/v1/2026.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}."
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<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 https://github.com/pzy2000/RepoGenesis.</abstract>
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%0 Conference Proceedings
%T RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository
%A Peng, Zhiyuan
%A Yin, Xin
%A Zhao, Pu
%A Yang, Fangkai
%A Wang, Lu
%A Jia, Ran
%A Chen, Xu
%A Lin, Qingwei
%A Rajmohan, Saravan
%A Zhang, Dongmei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F peng-etal-2026-repogenesis
%X 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 https://github.com/pzy2000/RepoGenesis.
%R 10.18653/v1/2026.acl-long.415
%U https://aclanthology.org/2026.acl-long.415/
%U https://doi.org/10.18653/v1/2026.acl-long.415
%P 9161-9189
Markdown (Informal)
[RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository](https://aclanthology.org/2026.acl-long.415/) (Peng et al., ACL 2026)
ACL
- Zhiyuan Peng, Xin Yin, Pu Zhao, Fangkai Yang, Lu Wang, Ran Jia, Xu Chen, Qingwei Lin, Saravan Rajmohan, and Dongmei Zhang. 2026. RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9161–9189, San Diego, California, United States. Association for Computational Linguistics.