BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Services

2026-07-14 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

BackendForge is a new benchmark comprising 56 contract-defined backend generation tasks, rewritten from real open-source applications, designed to evaluate agentic large language models (LLMs) in end-to-end software artifact generation. It requires LLMs to generate Dockerized services from natural-language specifications and OpenAPI contracts, which are then built, deployed, and evaluated solely through black-box HTTP tests. A multi-agent oracle co-evolution framework strengthens the test suite without introducing hidden requirements. Initial evaluations show that while the best-performing model, GPT-5.5, achieves 55.4% success under a base oracle, its success drops to 28.6% under the final, hardened oracle. This indicates current LLMs can implement many local API behaviors but struggle to produce complete, deployable backend services, particularly with state consistency, authorization, and validation.

Key takeaway

For AI Engineers developing agentic LLMs for software generation, you must shift evaluation beyond isolated code snippets to deployable, end-to-end services. Your models currently struggle with critical backend semantics like state consistency, authorization, and validation, even if they pass local API tests. Prioritize improving LLM capabilities in these areas, and consider integrating robust, contract-grounded multi-agent test co-evolution into your development and evaluation pipelines to expose subtle service-level defects early.

Key insights

Agentic LLMs struggle with end-to-end deployable backend service generation despite local API competence.

Principles

• Robust LLM evaluation requires deployable, end-to-end software artifacts.
• Oracle co-evolution strengthens benchmarks without hidden requirements.
• Local API success does not guarantee full service correctness.

Method

BackendForge constructs benchmarks by rewriting open-source apps into visible natural-language specs and OpenAPI contracts. A multi-agent system co-evolves test oracles and reference implementations, admitting tests only if specification-grounded and exposing reference defects.

In practice

• Use BackendForge to benchmark agentic LLM code generation.
• Focus LLM development on service-level semantics like state and auth.
• Implement multi-agent test generation for robust evaluation.

Topics

• Agentic LLMs
• Code Generation
• Backend Services
• Software Benchmarking
• OpenAPI
• Multi-Agent Systems

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.