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Journal Articles Journal of Big Data Year : 2021

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

Abstract

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.

Dates and versions

hal-03515722 , version 1 (06-01-2022)

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Cite

Bilal Elghadyry, Faissal Ouardi, Zineb Lotfi, Sébastien Verel. Efficient parallel derivation of short distinguishing sequences for nondeterministic finite state machines using MapReduce. Journal of Big Data, 2021, 8, pp.145. ⟨10.1186/s40537-021-00535-6⟩. ⟨hal-03515722⟩

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