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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

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Si no se descubren hasta la implementación, es mucho más costoso resolverlos que abordarlos temprano. Aunque la ambigüedad y la vaguedad son ampliamente estudiadas, se ha prestado menos atención a las suposiciones incrustadas en los requisitos que parecen claros y razonables. Proponemos LaydenLens, un nuevo tubo de seis capas de BERT-LLM y, a nuestro conocimiento, el primer marco diseñado para detectar, clasificar, cuestionar y resolver suposiciones ocultas en lugar de sólo indicar la ambigüedad de la bandera. Layer 1 utiliza la entropía de la BERT para localizar los intervalos no resueltos; Layer 2 utiliza el razonamiento final CoT-Prompted LLM para confirmar primero cada suposición y su riesgo de fallo; We Layer 3

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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