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HiddenLens: A Multi-Layer LLM Framework for Detecting and Resolving Hidden Assumptions in Software Requirements

Tanu Singh, aashna suman, Vinod Patidar · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29996 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

这种基于自然语言的Lay-Lay 软件要求往往会忽略有关性能、操作条件、用户行为和基于域的限制的数据假设,影响建筑、测试和部署。如果未被发现到实施之前,解决这些问题比提前解决更为昂贵。尽管隐含和模糊性被广泛研究,但对嵌入在要求中的假设的关注较少;我们建议LaydenLens,一个新的六层BERT-LLM管道,并根据我们的知识,设计的第一个框架,用于检测,分类,质疑和解决隐藏假设,而不是仅仅仅是标志性隐含。Layer 1使用Bert 注重引入来实现未解决的有效范围;Layer 2使用CoT-Ad-prompted LLM,以确认每个假设和分类失败的

Machine translation (Meta M2M100); the English original follows.

Abstract

Natural-language software requirements often omit assumptions about performance, operating conditions, user behaviour, and domain constraints, affecting architecture, testing, and deployment. If undiscovered until implementation, resolving them is far more costly than addressing them early. Although ambiguity and vagueness are widely studied, less attention has been given to assumptions embedded in requirements that appear clear and reasonable. We propose HiddenLens, a novel six-layer BERT-LLM pipeline and, to our knowledge, the first framework engineered to detect, classify, question, and resolve hidden assumptions rather than merely flag ambiguity. Layer 1 uses BERT attention entropy to localise unresolved spans; Layer 2 uses CoT-prompted LLM reasoning to articulate each assumption and its failure risk; Layer 3 uses fine-tuned BERT to classify assumptions into six non-functional requirement categories: performance, security, scalability, usability, reliability, and domain; Layer 4 generates ranked clarification options using FS LLM prompting; Layer 5 enables human validation through confirmation, editing, or rephrasing; and Layer 6 rewrites the requirement using the confirmed resolution, producing an explicit, testable, and traceable requirement. We also introduce the HiddenLens Corpus (HLC), comprising 312 requirements annotated with 847 hidden assumptions across six NFR categories. Inspired by PROMISE and PURE, HLC addresses the lack of annotated data for this task and is, to our knowledge, the first dataset developed specifically for it. HiddenLens outperforms keyword-based and QuARS-Adapted baselines by 71% and 61%, respectively, achieving an end-to-end F1-score of 0.79; its BERT-based NFR classifier achieves an F1-score of 0.847. These results indicate that HiddenLens can help analysts resolve hidden assumptions before they propagate through software development.

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Topics: Computer science, Vagueness, Ambiguity, Software, Layer (electronics), Classifier (UML)

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