Describing Homing and Distinguishing Sequences for Nondeterministic Finite State Machines via Synchronizing Automata

Kushik, Natalia; Yevtushenko, Nina

doi:10.1007/978-3-319-22360-5_16

Cited by 3 publications

(3 citation statements)

References 15 publications

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Section: Introductionmentioning

confidence: 94%

“…A similar work also appeared in (Kushik and Yevtushenko, 2015). However, (Kushik and Yevtushenko, 2015) considers nondeterministic finite state machines, whereas in our work we consider deterministic finite state machines. When the approach is considered as restricted to the deterministic case, it becomes possible to use efficient synchronizing heuristics developed for deterministic automata for computing homing sequences.…”

Section: Introductionmentioning

confidence: 94%

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Using Synchronizing Heuristics to Construct Homing Sequences

Çirişci

Emek

Sorguç

et al. 2019

Proceedings of the 7th International Conference on Model-Driven Engineering and Software Development

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Computing a shortest synchronizing sequence of an automaton is an NP-Hard problem. There are well-known heuristics to find short synchronizing sequences. Finding a shortest homing sequence is also an NP-Hard problem. Unlike existing heuristics to find synchronizing sequences, homing heuristics are not widely studied. In this paper, we discover a relation between synchronizing and homing sequences by creating an automaton called homing automaton. By applying synchronizing heuristics on this automaton we get short homing sequences. Furthermore, we adapt some of the synchronizing heuristics to construct homing sequences.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 94%

Using Synchronizing Heuristics to Construct Homing Sequences

Çirişci

Emek

Sorguç

et al. 2019

Proceedings of the 7th International Conference on Model-Driven Engineering and Software Development

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show abstract

“…Furthermore, the problem of deriving a separating sequence for the set of states of a deterministic FSM is PS-COMPLETE (Lee and Yannakakis (Lee & Yannakakis 1994) for complete machines, Hierons and Türker for partial machines (Hierons & Türker 2014)). For nondeterministic FSMs, Kushik and Yevtushenko (Kushik & Yevtushenko 2015) have shown that the problem of deriving a separating sequence for the set of states of a nondeterministic FSM is PS-COMPLETE. It is also known that in the worst case the length of a separating sequence even for two states is exponential w.r.t.…”

Section: Algorithm For Distinguishing Two Sets Of Efsm Configurationsmentioning

confidence: 99%

Distinguishing extended finite state machine configurations using predicate abstraction

El-Fakih

Yevtushenko

Bozga

et al. 2016

J Softw Eng Res Dev

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Background: Extended Finite State Machines (EFSMs) provide a powerful model for the derivation of functional tests for software systems and protocols. Many EFSM based testing problems, such as mutation testing, fault diagnosis, and test derivation involve the derivation of input sequences that distinguish configurations of a given EFSM specification. Method and Results: In this paper, a method is proposed for the derivation of a distinguishing sequence for two explicitly given or symbolically represented, possibly infinite, sets of EFSM configurations using a corresponding FSM abstraction that is derived based on finite sets of predicates and parameterized inputs. An abstraction function that maps configurations and transitions of the EFSM specification to corresponding abstract states and transitions of the abstraction FSM is proposed. Properties of the abstraction are established along with a discussion on a proper selection of the sets of predicates and parameterized inputs used while deriving an FSM abstraction. If no distinguishing sequence is found for the current FSM abstraction then a refined abstraction is constructed by extending the sets of predicates and parameterized inputs. Simple heuristics for the selection of additional predicates are discussed and application examples are provided. Conclusions: The work can be applied in various domains such as EFSM-based test derivation, mutation testing, and fault diagnosis.

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