Relational Action Bases: Formalization, Effective Safety Verification, and Invariants (Extended Version)

TL;DR

This paper introduces relational action bases (RABs), a flexible framework for modeling and verifying dynamic relational systems, and demonstrates an SMT-based safety verification approach that leverages invariants for correctness.

Contribution

It generalizes existing models by allowing unbounded relational states, quantified actions, and numerical data, and provides an effective verification method using SMT solvers.

Findings

Effective safety verification demonstrated on business process benchmarks.

The approach leverages invariants for full correctness.

Utilizes existing verification modules for practical implementation.

Abstract

Modeling and verification of dynamic systems operating over a relational representation of states are increasingly investigated problems in AI, Business Process Management, and Database Theory. To make these systems amenable to verification, the amount of information stored in each relational state needs to be bounded, or restrictions are imposed on the preconditions and effects of actions. We introduce the general framework of relational action bases (RABs), which generalizes existing models by lifting both these restrictions: unbounded relational states can be evolved through actions that can quantify both existentially and universally over the data, and that can exploit numerical datatypes with arithmetic predicates. We then study parameterized safety of RABs via (approximated) SMT-based backward search, singling out essential meta-properties of the resulting procedure, and showing how it can be realized by an off-the-shelf combination of existing verification modules of the state-of-the-art MCMT model checker. We demonstrate the effectiveness of this approach on a benchmark of data-aware business processes. Finally, we show how universal invariants can be exploited to make this procedure fully correct.