Parallel algorithms for reducing derivation time of distinguishing experiments for nondeterministic finite state machines

Many approaches have been proposed for deriving tests from finite state machine (FSM) specifications with respect to some established coverage criteria. A fundamental core problem in FSM-based testing relates to the derivation of input sequences that can distinguish states of an FSM specification, aka distinguishing sequences. A major effort in the construction of these sequences is based on the derivation of a successors search-tree labeled by sets of pairs of states of the given machine. We aim at reducing the time associated with such constructions through the use of state-of-the-art parallel technologies. Namely, we propose a parallel algorithm that we implement and evaluate on multicore CPUs and on many-core GPUs. We evaluate two alternative GPU implementations that use the CUDA and Thrust software platforms and a network of workstations based solution. The latter sports a workload partitioning based on Divisible Load Theory. A rigorous set of experiments highlights the differences of the proposed implementations in terms of execution time and speedup.