Tanu Singh, Aashna Suman, Vinod Patidar · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29996 · open-access PDF
자연 언어 소프트웨어 요구 사항은 종종 성능, 운영 조건, 사용자 행동 및 도메인 제한에 대한 가정을 무시하여 아키텍처, 테스트 및 배포에 영향을 미칩니다. 실행 전까지 발견되지 않은 경우, 이를 해결하는 것이 일찍 해결하는 것보다 훨씬 더 비싸습니다. 불확실성과 불확실성은 광범위하게 연구되지만 명확하고 합리적인 것처럼 보이는 요구 사항에 내장된 가정에 관심이 적습니다. 우리는 HiddenLens를 제안합니다.
Machine translation (Meta M2M100); the English original follows.
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 res
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