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The Computer Journal
Article . 2016 . Peer-reviewed
Data sources: Crossref
DBLP
Article . 2017
Data sources: DBLP
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Distinguishing Sequences for Distributed Testing: Preset Distinguishing Sequences

Authors: Robert M. Hierons; Uraz Cengiz Türker;

Distinguishing Sequences for Distributed Testing: Preset Distinguishing Sequences

Abstract

This paper concerns the problem of testing from a finite state machine (FSM) $M$ modelling a system that interacts with its environment at multiple physically distributed interfaces, called ports. We assume that the distributed test architecture is used: there is a local tester at each port, the tester at port $p$ only observes events at $p$ and the testers do not interact during testing. This paper formalizes the notion of an adaptive test strategy and what it means for an adaptive test strategy to be controllable. We provide algorithms to check whether a global strategy is controllable and to generate a controllable adaptive distinguishing sequence (ADS). We prove that controllable ADS existence is PSPACE-Hard and that the problem of deciding whether $M$ has a controllable ADS with length $\ell $ is NP-Hard. In practice, there is likely to be a polynomial upper bound on the length of ADS in which we are interested and for this case the decision problem is NP-Complete.

Country
United Kingdom
Related Organizations
Keywords

Distributed test architecture, Finite state machine, Preset distinguishing sequence, 004, 620

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
bronze