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https://dx.doi.org/10.4230/dag...
Article . 2010
License: CC BY
Data sources: Datacite
DBLP
Conference object . 2018
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Model-Based Testing for the Cloud

Authors: Bertolino, Antonia; Grieskamp, Wolfgang; Hierons, Robert; Le Traon, Yves; Legeard, Bruno; Muccini, Henry; Paradkar, Amit; +2 Authors

Model-Based Testing for the Cloud

Abstract

Software in the cloud is characterised by the need to be highly adaptive and continuously available. Incremental changes are applied to the deployed system and need to be tested in the field. Different configurations need to be tested. Higher quality standards regarding both functional and non-functional properties are put on those systems, as they often face large and diverse customer bases and/or are used as services from different peer service implementations. The properties of interest include interoperability, privacy, security, reliability, performance, resource use, timing constraints, service dependencies, availability, and so on. This paper discusses the state of the art in model-based testing of cloud systems. It focuses on two central aspects of the problem domain: (a) dealing with the adaptive and dynamic character of cloud software when tested with model-based testing, by developing new online and offline test strategies, and (b) dealing with the variety of modeling concerns for functional and non-functional properties, by devising a unified framework for them where this is possible. Having discussed the state of the art we identify challenges and future directions.

Country
Germany
Keywords

Model based testing, Cloud computing, 004, Non-functional properties

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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!
0
Average
Average
Average
Green