Parallel Algorithms for Generating Harmonised State Identifiers and Characterising Sets

Abstract

Abstract-Many automated finite state machine (FSM) based test generation algorithms require that a characterising set (CS) or a set of harmonised state identifiers (HSIs) is first produced. The only previously published algorithms for partial FSMs were brute-force algorithms with exponential worst case time complexity. This paper presents polynomial time algorithms and also massively parallel implementations of both the polynomial time algorithms and the brute-force algorithms. In the experiments the parallel algorithms scaled better than the sequential algorithms and took much less time. Interestingly, while the parallel version of the polynomial time algorithm was fastest for most sizes of FSMs, the parallel version of the brute-force algorithm scaled better due to lower memory requirements.

Parallel brute-force algorithm for deriving reset sequences from deterministic incomplete finite automata

“…Moreover, recently researchers proposed a parallel algorithm to accelerate the execution of a FA [35,36]. Recently massively parallel algorithms have also been presented for deriving state identification sequences from finite state machines [37,38]. Karahoda et al recently represented a fast version of an existing greedy RS generation algorithm ( [4]) for deterministic complete automata [39].…”

Accelerated Finite State Machine Test Execution Using GPUs

“…In recent years, Hierons and Trker have been using GPUs to accelerate the generation of testing sequences for FSMs based on unique I/O sequences [14], state harmonised state identifiers and characterising sets [13] and distinguishing sequences [15]. As far as we are aware, there is no existing work in using GPUs to accelerate the execution of FSM tests.…”

$\mathcal k$-branching uio sequences for partially specified observable non-deterministic fsms

“…The experiments indicated that the notion of Kbranching helps us make UIO generation more scalable. One particular line of future work is to extend this to other important sequences used in test generation such as adaptive distinguishing sequences, preset distinguishing sequences [43] characterizing sets and harmonized state identifiers [45]. There may also be scope to use SAT solvers or constraint solvers to generate K-UIOS, potentially adapting work that generates UIOs [46].…”