TACO: A Toolsuite for the Verification of Threshold Automata

2026-05-07

AI Summary

Verifying the correctness of fault-tolerant distributed algorithms with threshold-based mechanisms is highly challenging. This work proposes TACO, a novel verification toolkit that, for the first time, integrates multiple decidable logical fragments with two semi-decision procedures that transcend these fragment limitations into a unified, modular, and extensible verification framework. TACO enables modeling and fully automated verification of threshold automata, significantly improving verification efficiency. The approach has been successfully applied to several classic distributed algorithms, and experimental evaluation demonstrates its practical effectiveness and scalability.

Abstract

We present TACO, a toolsuite for the development and automatic verification of fault-tolerant and threshold-based distributed algorithms. Our toolsuite implements three approaches for model checking threshold automata in different decidable fragments known from the literature and two semi-decision procedures going beyond these decidable fragments. Moreover, TACO is a modular, extensible, and well-documented framework for developing algorithms and tools for threshold automata. We present important features, give an overview of the implemented algorithms, and evaluate their performance experimentally.

Problem

• threshold automata
• distributed algorithms
• fault tolerance
• formal verification
• model checking

Innovation

• threshold automata
• model checking
• fault-tolerant algorithms
• decidable fragments
• verification toolsuite