TACO: A Toolsuite for the Verification of Threshold Automata
For researchers and engineers developing distributed algorithms, TACO provides a modular, extensible verification framework, but it is an incremental tool integration rather than a breakthrough.
TACO is a toolsuite for verifying fault-tolerant, threshold-based distributed algorithms using threshold automata, implementing three decidable fragments and two semi-decision procedures. Experimental evaluation demonstrates its performance across various benchmarks.
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.