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https://doi.org/10.1109/ares.2...
Article . 2011 . Peer-reviewed
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Compositional synthesis of temporal fault trees from state machines

Authors: Nidhal Mahmud; Martin Walker; Yiannis Papadopoulos;

Compositional synthesis of temporal fault trees from state machines

Abstract

Dependability analysis of a dynamic system which is embedded with several complex interrelated components raises two main problems. First, it is difficult to represent in a single coherent and complete picture how the system and its constituent parts behave in conditions of failure. Second, the analysis can be unmanageable due to a considerable number of failure events, which increases with the number of components involved. To remedy this problem, in this paper we outline an analysis approach that converts failure behavioural models (state machines) to temporal fault trees (TFTs), which can then be analysed using Pandora -- a recent technique for introducing temporal logic to fault trees. The approach is compositional and potentially more scalable, as it relies on the synthesis of large system TFTs from smaller component TFTs. We show, by using a Generic Triple Redundant (GTR) system, how the approach enables a more accurate and full analysis of an increasingly complex system.

Country
United Kingdom
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Keywords

Computer Networks and Communications, Hardware and Architecture, Software, 004

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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!
14
Average
Top 10%
Average
Green
bronze