Constraint Decay: The Fragility of LLM Agents in Back End Code Generation

Computer Science > Software Engineering arXiv:2605.06445 (cs) [Submitted on 7 May 2026] Title:Constraint Decay: The Fragility of LLM Agents in Backend Code Generation Authors:Francesco Dente, Dario Satriani, Paolo Papotti View a PDF of the paper titled Constraint Decay: The Fragility of LLM Agents in Backend Code Generation, by Francesco Dente and 2 other authors View PDF HTML (experimental)

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. Capable configurations lose 30 points on average in assertion pass rates from baseline to fully specified tasks, while some weaker configurations approach zero. Framework sensitivity analysis exposes significant performance disparities: agents 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)
Cite as:	arXiv:2605.06445 [cs.SE]
	(or arXiv:2605.06445v1 [cs.SE] for this version)
	https://doi.org/10.48550/arXiv.2605.06445 Focus to learn more arXiv-issued DOI via DataCite

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From: Paolo Papotti [view email]
[v1] Thu, 7 May 2026 15:44:40 UTC (401 KB)
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