Verifiable Checks for Business Rule Consistency

2026-07-31

AI Summary

This work addresses the challenge of maintaining consistency between natural language business rules and their programmatic implementations in large-scale systems by proposing the SIRNA framework. SIRNA is the first approach to integrate large language models with SMT solvers, automatically translating natural language rules into formal SMT specifications and verifying their consistency against SMT representations derived from code. By synergistically combining natural language understanding with formal verification, the method substantially reduces both false positive and false negative rates. Evaluated on a tax cost calculation case study, SIRNA demonstrates high precision and interpretability, offering a novel paradigm for ensuring the fidelity of business rule enforcement in complex software systems.

Abstract

Maintaining consistency between natural language documentation of business rules and their evolving internal implementations is a significant challenge in large-scale systems. We present SIRNA, a tool and framework for checking such consistency using SMT solvers. Using the case study of cost calculations in tax domains, we demonstrate a three-part system that combines large language models (LLMs) with formal verification methods. SIRNA translates natural language documentation into candidate SMT formulas using LLMs, followed by checks to validate the translations. Then, corresponding business rules are converted into equivalent SMT representations and validated against the natural language formalizations. Our method is generalizable to domains where business logic exists in both natural language documentation and programmatic implementation. Compared to baseline evaluations, SIRNA significantly reduces the number of false positives and false negatives while offering explainability for its findings.

Problem

• business rule consistency
• natural language documentation
• formal verification
• SMT solvers
• large-scale systems

Innovation

• SMT solvers
• large language models
• business rule consistency
• formal verification
• explainable AI