Full verification of learning-enabled cyber-physical systems (CPS) has long been intractable due to challenges including black-box components and complex real-world environments. Existing tools either provide formal guarantees for limited types of systems or test the system as a monolith, but no general framework exists for compositional analysis of learning-enabled CPS using varied verification techniques over complex real-world environments. This paper introduces a tool, ScenicProver, that aims to fill this gap by enabling the construction of sound, formally-checked assurance cases for CPS. ScenicProver is backed by a formal theory, which we lay out in this paper. ScenicProver supports: (1) environment modeling using the Scenic probabilistic programming language; (2) compositional system modeling with clear component interfaces, ranging from interpretable code to black boxes; (3) assume-guarantee contracts over those components using an extension of Linear Temporal Logic containing arbitrary Scenic expressions; (4) evidence generation through testing, formal proofs in Lean 4, and external assumptions; (5) sound combination of generated evidence using contract operators with Pacti; and (6) automatic generation of assurance cases tracking the provenance of system-level guarantees. We demonstrate its effectiveness through two case studies: one on an automatic emergency braking system with sensor fusion and one on drone safety. By leveraging manufacturer guarantees for sensors and focusing testing effort on uncertain conditions, our approach enables stronger probabilistic guarantees than monolithic testing with the same computational budget.

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