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约束衰减：LLM 智能体在后端代码生成中的脆弱性
该论文系统评估 LLM 智能体在多文件后端代码生成中对结构约束的遵循能力。作者在 8 个 Web 框架上固定统一 API 契约，设置 80 个绿地生成任务与 20 个功能实现任务，结合端到端行为测试与静态验证器进行双重评估。结果显示存在“约束衰减”现象：随结构要求累积，智能体断言通过率从基线到完全指定任务平均下降 27.28 分。框架敏感性分析表明，中等能力模型在 Flask 等最小化、显式框架上表现较好，而在 FastAPI、Django 等约定密集环境中平均表现明显更差。错误分析将数据层缺陷（如查询组合错误、ORM 运行时违规）列为主要根因。作者认为，同时满足功能性与结构性要求仍是编码智能体的关键开放挑战。
AI推荐理由 arXiv 论文（cs.SE/cs.AI），有明确作者、提交与修订记录及可验证实验设计：固定统一 API 契约，覆盖 8 个 Web 框架、80 个绿地生成任务与 20 个功能实现任务，采用端到端行为测试与静态验证双重评估。核心新信息是提出并量化“约束衰减”现象——随结构约束累积，智能体断言通过率平均下降 27.28 分；并给出框架敏感性差异（Flask 等显式框架表现较好，FastAPI、Django 等约定密集框架明显更差）及错误根因（数据层缺陷，如查询构造错误与 ORM 运行时违规）。局限在于摘要未披露具体模型清单、样本规模细节与统计显著性，结论基于其自建任务集，尚需复现验证。
82
正文
Computer Science > Software Engineering
arXiv:2605.06445 (cs)
[Submitted on 7 May 2026 (v1), last revised 18 Sep 2026 (this version, v2)]
Title:Constraint Decay: The Fragility of LLM Agents in Backend Code Generation
Authors:Francesco Dente, Dario Satriani, Paolo Papotti
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Abstract:Large Language Model (LLM) agents demonstrate strong performance in autonomous code generation under loose specifications. However, production-grade software requires strict adherence to structural constraints, such as architectural patterns, databases, and object-relational mappings. Existing benchmarks often overlook these non-functional requirements, rewarding functionally correct but structurally arbitrary solutions. We present a systematic study evaluating how well agents handle structural constraints in multi-file backend generation. By fixing a unified API contract across 80 greenfield generation tasks and 20 feature-implementation tasks spanning eight web frameworks, we isolate the effect of structural complexity using a dual evaluation with end-to-end behavioral tests and static verifiers. Our findings reveal a phenomenon of constraint decay: as structural requirements accumulate, agent performance exhibits a substantial decline. Evaluated configurations lose 27.28 points on average in assertion pass rates from baseline to fully specified tasks. Framework sensitivity analysis exposes performance disparities: mid-tier models succeed in minimal, explicit frameworks (e.g., Flask) but perform substantially worse on average in convention-heavy environments (e.g., FastAPI, Django). Finally, error analysis identifies data-layer defects (e.g., incorrect query composition and ORM runtime violations) as the leading root causes. This work highlights that jointly satisfying functional and structural requirements remains a key open challenge for coding agents.
Subjects:
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
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arXiv:2605.06445 [cs.SE]
(or
arXiv:2605.06445v2 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2605.06445
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arXiv-issued DOI via DataCite
Submission history
From: Francesco Dente Dente [view email]
[v1]
Thu, 7 May 2026 15:44:40 UTC (401 KB)
[v2]
Fri, 18 Sep 2026 13:05:56 UTC (410 KB)
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