Checking Experiments for Finite State Machines with Symbolic Inputs

Graph-based slicing that eliminates unnecessary tran- sitions and nodes produces an output that will not be a sub graph of the original. Although we term this amorphous slicing it

The heuristic search results suggest that combining the search for feasible transi- tion paths (FTPs), examined in Chapter 4, and the search for unique input /output sequences

Index Terms Automata F.1.1.a, Classes defined by grammars or automata F.4.3.b, Machine learning I.2.6.g, Language acquisition I.2.6.h, Language models I.2.7.c, Language parsing

Abstract Probabilistic finite-state machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational

Further, instead of translating the HFSM to ordinary state machines and using classical syn- thesis tools on the resulting FSM, we here present algorithms that solve the

We conjecture that N-gram based test sequence generation will produce less infeasible test sequences (hence, higher coverage) than graph visit or random model traversal.. With these

to symbolic input finite state machine (SIFSM). SIFSM [21] is an extension of FSM with inputs specified with predicates on input variables possibly having infinite do- mains,

propose a complete model-based equivalence testing strategy applicable to reac- tive systems with large, possibly infinite input data types but finite internal and output data..