Efficient parallel derivation of short distinguishing sequences for nondeterministic finite state machines using MapReduce

Abstract

Distinguishing sequences are widely used in finite state machine-based conformance testing to solve the state identification problem. In this paper, we address the scalability issue encountered while deriving distinguishing sequences from complete observable nondeterministic finite state machines by introducing a massively parallel MapReduce version of the well-known Exact Algorithm. To the best of our knowledge, this is the first study to tackle this task using the MapReduce approach. First, we give a concise overview of the well-known Exact Algorithm for deriving distinguishing sequences from nondeterministic finite state machines. Second, we propose a parallel algorithm for this problem using the MapReduce approach and analyze its communication cost using Afrati et al. model. Furthermore, we conduct a variety of intensive and comparative experiments on a wide range of finite state machine classes to demonstrate that our proposed solution is efficient and scalable.

Accelerating Finite State Machine-Based Testing Using Reinforcement Learning

“…In such scenarios, instead of an ADS, we may use PDSs. Therefore, many test generation algorithms require an RS and a PDS to construct a test [1], [16], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29]. However, the state-of-the-art approaches in deriving these sequences cannot process large FSMs.…”