Iterative minimization of partial finite state machines

Abstract

AbstractFinite state machines have been used in many contexts, e.g., test generation, system modeling, language processing. In these applications, the size of the machine has a great impact on the complexity and quality of the results. Thus, it is desirable to obtain a machine without redundant states, i.e., to minimize the machine. However, the minimization of partially specified finite state machines is a known NP-complete problem, and many heuristics to obtain feasible solutions have been proposed. In this paper, we propose an approach to minimize partial finite state machines based on the selection of compatible states using maximum cliques on the distinction graph. We experimented with our method comparing it with previous methods and the data suggest that our approach is more efficient, generating good results in less time.