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    <abstract>Large language models (LLMs) are increasingly used in agentic coding settings, where they can inspect files, execute commands, run tests, observe failures, and iteratively revise code. This shift raises a central evaluation question: can an agentic LLM generate an end-to-end software artifact that is both deployable and behaviorally correct under execution? Backend services provide a controlled but realistic substrate for this evaluation. Their APIs expose application-level executable semantics, and deployed behavior can be checked deterministically against an OpenAPI contract through black-box HTTP interactions. We introduce , a benchmark of 56 contract-defined backend generation tasks rewritten from real open-source applications. Given a visible specification and an OpenAPI contract, an LLM must generate a Dockerized service that is built, deployed, and evaluated only through HTTP tests. To strengthen evaluation without introducing hidden requirements, uses a test agent and a code agent to co-evolve the test oracle and reference service, where the test agent proposes specification-grounded backend tests and the code agent repairs the reference implementation. Although the best-performing model, GPT-5.5, succeeds on 55.4% of tasks under the base oracle, it succeeds on only 28.6% under the final oracle. This gap suggests that current LLMs can implement many local API behaviors, but still struggle to produce complete backend services.</abstract>
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%0 Conference Proceedings
%T BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Services
%A Guo, Yuzhe
%A Wu, Mengzhou
%A Cao, Yuan
%A Wei, Jialei
%A Ran, Dezhi
%A Yang, Wei
%A Xie, Tao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F guo-etal-2026-backendforge
%X Large language models (LLMs) are increasingly used in agentic coding settings, where they can inspect files, execute commands, run tests, observe failures, and iteratively revise code. This shift raises a central evaluation question: can an agentic LLM generate an end-to-end software artifact that is both deployable and behaviorally correct under execution? Backend services provide a controlled but realistic substrate for this evaluation. Their APIs expose application-level executable semantics, and deployed behavior can be checked deterministically against an OpenAPI contract through black-box HTTP interactions. We introduce , a benchmark of 56 contract-defined backend generation tasks rewritten from real open-source applications. Given a visible specification and an OpenAPI contract, an LLM must generate a Dockerized service that is built, deployed, and evaluated only through HTTP tests. To strengthen evaluation without introducing hidden requirements, uses a test agent and a code agent to co-evolve the test oracle and reference service, where the test agent proposes specification-grounded backend tests and the code agent repairs the reference implementation. Although the best-performing model, GPT-5.5, succeeds on 55.4% of tasks under the base oracle, it succeeds on only 28.6% under the final oracle. This gap suggests that current LLMs can implement many local API behaviors, but still struggle to produce complete backend services.
%U https://aclanthology.org/2026.findings-emnlp.2236/
%P 47861-47876

Markdown (Informal)

[BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Services](https://aclanthology.org/2026.findings-emnlp.2236/) (Guo et al., Findings 2026)

• BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Services (Guo et al., Findings 2026)

ACL

• Yuzhe Guo, Mengzhou Wu, Yuan Cao, Jialei Wei, Dezhi Ran, Wei Yang, and Tao Xie. 2026. BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Services. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 47861–47876, Budapest, Hungary. Association for Computational Linguistics.