The HIVE Tool for Informed Swarm State Space Exploration

TL;DR

The paper introduces the HIVE tool and two algorithms that enhance swarm state space exploration by informing workers which parts to explore, improving scalability especially when bugs are absent.

Contribution

It presents a novel tool and algorithms that coordinate informed swarm exploration, allowing bounded and efficient state space analysis in formal models.

Findings

Improved scalability in state space exploration when bugs are absent.

Effective coordination of informed swarm searches using HIVE.

Integration with LTSmin enhances preprocessing and search execution.

Abstract

Swarm verification and parallel randomised depth-first search are very effective parallel techniques to hunt bugs in large state spaces. In case bugs are absent, however, scalability of the parallelisation is completely lost. In recent work, we proposed a mechanism to inform the workers which parts of the state space to explore. This mechanism is compatible with any action-based formalism, where a state space can be represented by a labelled transition system. With this extension, each worker can be strictly bounded to explore only a small fraction of the state space at a time. In this paper, we present the HIVE tool together with two search algorithms which were added to the LTSmin tool suite to both perform a preprocessing step, and execute a bounded worker search. The new tool is used to coordinate informed swarm explorations, and the two new LTSmin algorithms are employed for preprocessing a model and performing the individual searches.