Neurosymbolic Auditing of Natural-Language Software Requirements
Natural-language software requirements are often ambiguous, inconsistent, and underspecified; in safety-critical domains, these defects propagate into formal models that verify the wrong specification and into implementations that ship unsafe behavior. We show that large language models, equipped with an SMT solver, can audit such requirements: translating them into formal logic, detecting ambiguity through stochastic variation in the generated formalization, and exposing inconsistency, vacuousness, and safety violations through solver queries on the resulting specification. We present VERIMED, a neurosymbolic pipeline that operationalizes this idea for medical-device software requirement...
| Authors | Bethel Hall, William Eiers |
|---|---|
| Primary category | cs.SE |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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