scholarly journals Deriving adaptive distinguishing sequences for Finite State Machines

2018 ◽  
Vol 30 (4) ◽  
pp. 139-154 ◽  
Author(s):  
A.S. Tvardovskii ◽  
◽  
N.V. Yevtushenko ◽  
Keyword(s):  
Finite State Machines ◽  
State Machines ◽  
Distinguishing Sequences ◽  
Finite State

scholarly journals Incomplete Distinguishing Sequences for Finite State Machines

2015 ◽  
Vol 58 (11) ◽  
pp. 3089-3113 ◽  
Author(s):  
Robert M. Hierons ◽  
Uraz Cengiz Türker
Keyword(s):  
Finite State Machines ◽  
State Machines ◽  
Distinguishing Sequences ◽  
Finite State

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

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Bilal Elghadyry ◽  
Faissal Ouardi ◽  
Zineb Lotfi ◽  
Sébastien Verel
Keyword(s):  
Finite State Machine ◽  
Exact Algorithm ◽  
Finite State Machines ◽  
Identification Problem ◽  
State Machine ◽  
State Machines ◽  
State Identification ◽  
Distinguishing Sequences ◽  
Wide Range ◽  
Finite State

AbstractDistinguishing 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.

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