Compositional Automata Learning of Timed Synchronous Systems

Abstract

Active model learning is a crucial technique for automatically constructing formal models of black-box timed systems. However, existing learning approaches typically construct a single monolithic automaton for a timed system with multiple synchronous components, obscuring its component structure and hindering component-level analysis. We propose a compositional learning approach that constructs a deterministic real-time automaton (RTA) for each component with access only to the target system as a whole. Upon termination, the learned RTAs form a network that exhibits exactly the same timed behaviors as the target system. Within the respective resource limits, our implementation terminates successfully on 213 of the 240 (88.8%) randomly generated benchmark instances and all six adapted case-study benchmarks, and requires less learning time than monolithic approaches on some instances.