Stateless and Rule-Based Verification For Compliance Checking Applications

TL;DR

This paper introduces SARV, a formal logic-based framework for compliance verification that is tailored for stateless, rule-based systems, demonstrating its effectiveness through a smart city protocol case study and outperforming machine learning methods.

Contribution

The paper presents SARV, a novel formal framework for stateless, rule-based verification, specifically designed for compliance checking applications, with a case study and experimental validation.

Findings

SARV outperforms machine learning methods on a large dataset

The framework effectively models protocol compliance in smart cities

Experimental results show improved verification efficiency

Abstract

Underlying computational model has an important role in any computation. The state and transition (such as in automata) and rule and value (such as in Lisp and logic programming) are two comparable and counterpart computational models. Both of deductive and model checking verification techniques are relying on a notion of state and as a result, their underlying computational models are state dependent. Some verification problems (such as compliance checking by which an under compliance system is verified against some regulations and rules) have not a strong notion of state nor transition. Behalf of it, these systems have a strong notion of value symbols and declarative rules defined on them. SARV (Stateless And Rule-Based Verification) is a verification framework that designed to simplify the overall process of verification for stateless and rule-based verification problems (e.g. compliance checking). In this paper, a formal logic-based framework for creating intelligent compliance checking systems is presented. We define and introduce this framework, report a case study and present results of an experiment on it. The case study is about protocol compliance checking for smart cities. Using this solution, a Rescue Scenario use case and its compliance checking are sketched and modeled. An automation engine for and a compliance solution with SARV are introduced. Based on 300 data experiments, the SARV-based compliance solution outperforms famous machine learning methods on a 3125-records software quality dataset.