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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Reliability prediction for fault-tolerant software architectures

Authors: Franz Brosch; Barbora Buhnova; Heiko Koziolek; Ralf H. Reussner;

Reliability prediction for fault-tolerant software architectures

Abstract

Software fault tolerance mechanisms aim at improving the reliability of software systems. Their effectiveness (i.e., reliability impact) is highly application-specific and depends on the overall system architecture and usage profile. When examining multiple architecture configurations, such as in software product lines, it is a complex and error-prone task to include fault tolerance mechanisms effectively. Existing approaches for reliability analysis of software architectures either do not support modelling fault tolerance mechanisms or are not designed for an efficient evaluation of multiple architecture variants. We present a novel approach to analyse the effect of software fault tolerance mechanisms in varying architecture configurations. We have validated the approach in multiple case studies, including a large-scale industrial system, demonstrating its ability to support architecture design, and its robustness against imprecise input data.

Country
Germany
Keywords

ddc:004, DATA processing & computer science, info:eu-repo/classification/ddc/004, 004

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    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).
    20
    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.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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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!
20
Top 10%
Top 10%
Top 10%
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