Formally verifying infinite-state systems can be a daunting task, especially when it comes to reasoning about quantifiers. In particular, quantifier alternations in conjunction with function symbols can create function cycles that result in infinitely many ground terms, making it difficult for solvers to instantiate quantifiers and causing them to diverge. This can leave users with no useful information on how to proceed. To address this issue, we propose an interactive verification methodology that uses a relational abstraction technique to mitigate solver divergence in the presence of quantifiers. This technique abstracts functions in the verification conditions (VCs) as one-to-one relations, which avoids the creation of function cycles and the resulting proliferation of ground terms. Relational abstraction is sound and guarantees correctness if the solver cannot find counter-models. However, it may also lead to false counterexamples, which can be addressed by refining the abstraction and requiring the existence of corresponding elements. In the domain of distributed protocols, we can refine the abstraction by diagnosing counterexamples and manually instantiating elements in the range of the original function. If the verification conditions are correct, there always exist finitely many refinement steps that eliminate all spurious counter-models, making the approach complete. We applied this approach in Ivy to verify the safety properties of consensus protocols and found that: (1) most verification goals can be automatically verified using relational abstraction, while SMT solvers often diverge when given the original VC, (2) only a few manual instantiations were needed, and the counterexamples provided valuable guidance for the user compared to timeouts produced by the traditional approach, and (3) the technique can be used to derive efficient low-level implementations of tricky algorithms.

Proof automation can substantially increase productivity in formal verification of complex systems. However, unpredictablility of automated provers in handling quantified formulas presents a major hurdle to usability of these tools. We propose to solve this problem not by improving the provers, but by using a modular proof methodology that allows us to produce decidable verification conditions. Decidability greatly improves predictability of proof automation, resulting in a more practical verification approach. We apply this methodology to develop verified implementations of distributed protocols, demonstrating its effectiveness.