Analyzing second-order effects between optimizations for system-level test-based model generation

Tiziana Margaria; Harald Raffelt; Bernhard Steffen

doi:10.1109/TEST.2005.1584006

Analyzing second-order effects between optimizations for system-level test-based model generation

Tiziana Margaria, Harald Raffelt, Bernhard Steffen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Test-based model generation by classical automata learning is very expensive. It requires an impractically large number of queries to the system, each of which must be implemented as a system-level test case. Key towards the tractability of observation based model generation are powerful optimizations exploiting different kinds of expert knowledge in order to drastically reduce the number of required queries, and thus the testing effort. In this paper, we present a thorough experimental analysis of the second-order effects between such optimizations in order to maximize their combined impact.

Original language	English
Title of host publication	IEEE International Test Conference, Proceedings, ITC 2005
Pages	461-467
Number of pages	7
DOIs	https://doi.org/10.1109/TEST.2005.1584006
Publication status	Published - 2005
Externally published	Yes
Event	IEEE International Test Conference, ITC 2005 - Austin, TX, United States Duration: 8 Nov 2005 → 10 Nov 2005

Publication series

Name	Proceedings - International Test Conference
Volume	2005
ISSN (Print)	1089-3539

Conference

Conference	IEEE International Test Conference, ITC 2005
Country/Territory	United States
City	Austin, TX
Period	8/11/05 → 10/11/05

Access to Document

10.1109/TEST.2005.1584006

Cite this

@inproceedings{eca1cb7a2fdb4be3998da45a0556e71d,

title = "Analyzing second-order effects between optimizations for system-level test-based model generation",

abstract = "Test-based model generation by classical automata learning is very expensive. It requires an impractically large number of queries to the system, each of which must be implemented as a system-level test case. Key towards the tractability of observation based model generation are powerful optimizations exploiting different kinds of expert knowledge in order to drastically reduce the number of required queries, and thus the testing effort. In this paper, we present a thorough experimental analysis of the second-order effects between such optimizations in order to maximize their combined impact.",

author = "Tiziana Margaria and Harald Raffelt and Bernhard Steffen",

year = "2005",

doi = "10.1109/TEST.2005.1584006",

language = "English",

isbn = "0780390393",

series = "Proceedings - International Test Conference",

pages = "461--467",

booktitle = "IEEE International Test Conference, Proceedings, ITC 2005",

note = "IEEE International Test Conference, ITC 2005 ; Conference date: 08-11-2005 Through 10-11-2005",

}

Margaria, T, Raffelt, H & Steffen, B 2005, Analyzing second-order effects between optimizations for system-level test-based model generation. in IEEE International Test Conference, Proceedings, ITC 2005., 1584006, Proceedings - International Test Conference, vol. 2005, pp. 461-467, IEEE International Test Conference, ITC 2005, Austin, TX, United States, 8/11/05. https://doi.org/10.1109/TEST.2005.1584006

Analyzing second-order effects between optimizations for system-level test-based model generation. / Margaria, Tiziana; Raffelt, Harald; Steffen, Bernhard.
IEEE International Test Conference, Proceedings, ITC 2005. 2005. p. 461-467 1584006 (Proceedings - International Test Conference; Vol. 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Raffelt, Harald

AU - Steffen, Bernhard

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N2 - Test-based model generation by classical automata learning is very expensive. It requires an impractically large number of queries to the system, each of which must be implemented as a system-level test case. Key towards the tractability of observation based model generation are powerful optimizations exploiting different kinds of expert knowledge in order to drastically reduce the number of required queries, and thus the testing effort. In this paper, we present a thorough experimental analysis of the second-order effects between such optimizations in order to maximize their combined impact.

AB - Test-based model generation by classical automata learning is very expensive. It requires an impractically large number of queries to the system, each of which must be implemented as a system-level test case. Key towards the tractability of observation based model generation are powerful optimizations exploiting different kinds of expert knowledge in order to drastically reduce the number of required queries, and thus the testing effort. In this paper, we present a thorough experimental analysis of the second-order effects between such optimizations in order to maximize their combined impact.

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M3 - Conference contribution

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SN - 9780780390393

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ER -

Analyzing second-order effects between optimizations for system-level test-based model generation

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