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Electronic Notes in Theoretical Computer Science
Article . 2005 . Peer-reviewed
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Abstractions for Model-Based Testing

Authors: Prenninger, Wolfgang; Pretschner, Alexander;

Abstractions for Model-Based Testing

Abstract

The idea of model-based testing is to compare the I/O behavior of an explicit behavior model with that of a system under test. This requires the model to be valid. If the model is a simplification of the SUT, then it is easier to check the model and use it for subsequent test case generation than to directly check the SUT. In this case, the different levels of abstraction must be bridged. Not surprisingly, experience shows that choosing the right level of abstraction is crucial to the success of model-based testing. We argue that models for specification purposes, models for test generation, and models for full code generation are likely to be different. The paper classifies and discusses different abstractions. It is intended as a step towards guidelines for those who build behavior models to the end of testing.

Electronic Notes in Theoretical Computer Science, 116

ISSN:1571-0661

Country
Switzerland
Keywords

Model-basedtesting; SUT; Verification; Specification, testing, model, fault, Verification, Model-based testing, SUT, Theoretical Computer Science, Model-basedtesting, specification, verification, Specification, Computer Science(all), ddc: ddc:

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    influence
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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!
16
Average
Top 10%
Top 10%
Green
gold