🤖 AI Summary
Existing simulation-based compositional abstraction techniques for timed systems alleviate state-space explosion in model checking but lack support for broadcast synchronization and cannot simultaneously integrate broadcast synchronization, binary synchronization, shared variables, and committed locations within a unified parallel composition operator. Method: This paper proposes the first compositional abstraction framework supporting broadcast synchronization, built upon simulation relation theory to define UPPAAL-compatible compositional semantics for timed automata; it introduces a lightweight constraint—“shared variables remain unchanged upon receiving a broadcast”—to balance abstraction precision and scalability. Contribution/Results: The framework enables sound and scalable compositional verification of broadcast-based timed systems. Experimental evaluation on two case studies demonstrates significant performance gains over monolithic model checking, effectively mitigating state-space explosion while preserving correctness guarantees.
📝 Abstract
Simulation-based compositional abstraction effectively mitigates state space explosion in model checking, particularly for timed systems. However, existing approaches do not support broadcast synchronization, an important mechanism for modeling non-blocking one-to-many communication in multi-component systems. Consequently, they also lack a parallel composition operator that simultaneously supports broadcast synchronization, binary synchronization, shared variables, and committed locations. To address this, we propose a simulation-based compositional abstraction framework for timed systems, which supports these modeling concepts and is compatible with the popular UPPAAL model checker. Our framework is general, with the only additional restriction being that the timed automata are prohibited from updating shared variables when receiving broadcast signals. Through two case studies, our framework demonstrates superior verification efficiency compared to traditional monolithic methods.