🤖 AI Summary
This study addresses the challenges of aligning Python programs with formal specifications and verifying backward compatibility following component updates. To this end, this work proposes a source-preserving verification framework that expresses contracts through Python annotations and automatically generates proof artifacts in Dafny and Lean, thereby unifying development and functional verification at the source-code level. Furthermore, relational product techniques are introduced to check compatibility after component updates. The primary contribution is an end-to-end workflow bridging source-level proofs and compatibility verification, ensuring rigorous formal guarantees while preserving executable semantics. The associated codebase has been made publicly available.
📝 Abstract
Keeping a Python program and its formal guarantees aligned is a continuing maintenance problem. Specifications must describe the implementation that actually runs, and updates must preserve the behavior on which existing callers depend. VeriPy brings these obligations into a common workflow for annotated Python components. Developers and agents express contracts, invariants, and proof hooks as Python comments, then refine checked auxiliary lemmas using source-located diagnostics. Direct encoders produce Dafny or Lean artifacts while retaining admitted executable bodies and explicit models of their dependencies. A relational product checks whether an updated component still admits old inputs and preserves returned values and modeled exceptions. The resulting workflow connects source-level proof development with functional verification and backward compatibility, recording the assumptions under which each guarantee applies. The code is available at https://github.com/astrio-labs/veripy.