发表机构
Western University（西安大略大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出神经符号多智能体架构结合三值评分框架，消除LLM生成需求的结构不一致性，在37个项目愿景中实现94.6%的结构一致性消除，为LLM在需求工程中的安全部署提供支持。
AI 中文摘要
背景：大语言模型（LLMs）为自动化需求获取提供了自然语言灵活性，但常生成结构无效的需求和逻辑不一致的内容，缺乏形式化正确性保证。目标：本研究旨在消除LLM生成需求中的逻辑不一致性，确保结构一致性，同时在形式化领域模型中量化LLM预验证决策的不确定性。方法：我们提出一种神经符号多智能体架构，将面向对象需求创作与管理方法（OOMRAM）格付诸实施。LLM作为格遍历的非确定性启发式工具，确定性符号验证器则强制执行所有结构约束。我们引入三值（T、I、F，即真、不确定、假）框架，在验证前后对LLM的需求决策进行分类和评分。结果：在11个应用家族的37个自然语言项目愿景上进行评估，该系统在37个案例中完全消除了35个案例的结构不一致性（94.6%），剩余2个案例仅存在6个未解决的结构错误（占决策总数的0.39%），原因是迭代次数限制。三值分析显示，所有决策中有24.7%为不确定——即结构有效但属于利益相关者未明确要求的自由选择。结论：将结构完整性任务交由确定性符号层处理，可成功保证结构一致性，而三值分类为衡量神经不确定性提供了形式化方法，有助于在形式化需求工程中安全部署LLM。
英文摘要
Context: Large Language Models (LLMs) offer natural-language flexibility for automated requirements elicitation but frequently generate structurally invalid requirements and logical inconsistencies, lacking formal correctness guarantees. Objectives: This study aims to eliminate logical inconsistencies and enforce structural conformance in LLM-generated requirements while quantifying the LLM's pre-validation decision uncertainty within a formal domain model. Methods: We present a neuro-symbolic multi-agent architecture that operationalizes the Object-Oriented Method for Requirements Authoring and Management (OOMRAM) lattice. The LLM acts as a non-deterministic heuristic for lattice traversal, while a deterministic symbolic validator enforces all structural constraints. We introduce a three-valued (T, I, F) -- Truth, Indeterminacy, Falsity -- framework to classify and score the LLM's requirement decisions before and after validation. Results: Evaluated across 37 natural-language project visions in eleven application families, the system completely eliminated structural inconsistencies in 35 out of 37 cases (94.6%), with the remaining two containing only 6 unresolved structural errors (0.39% of decisions) due to iteration limits. Three-valued analysis revealed that 24.7% of all decisions are indeterminate -- structurally valid but discretionary choices not explicitly mandated by the stakeholder. Conclusion: Offloading structural integrity to a deterministic symbolic layer successfully guarantees structural conformance, while the three-valued classification provides a formal way to measure neural uncertainty, facilitating safe LLM deployment in formal requirements engineering.
