Model-Based Testing of Probabilistic Systems with Stochastic Time

Marcus Gerhold; Mariëlle Stoelinga

doi:10.1007/978-3-319-61467-0_5

Model-Based Testing of Probabilistic Systems with Stochastic Time

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

3 Citations (Scopus)

99 Downloads (Pure)

Abstract

This paper presents a model-based testing framework for black-box probabilistic systems with stochastic continuous time. Markov automata are used as an underlying model. We show how to generate, execute and evaluate test cases automatically from a probabilistically timed requirements model. In doing so, we connect classical ioco-theory with statistical hypothesis testing; our ioco-style algorithms test for functional behaviour, while χ2 hypothesis tests and confidence interval estimations assess the statistical correctness of the system.

A crucial development are the classical soundness and completeness properties of our framework. Soundness states that test cases assign the correct verdict, while completeness states that our methods are powerful enough to discover each discrepancy in functional or statistical misbehaviour, up to arbitrary precision.

We illustrate our framework via the Bluetooth device discovery protocol.

Original language	English
Title of host publication	Tests and Proofs
Subtitle of host publication	11th International Conference, TAP 2017, Held as Part of STAF 2017, Marburg, Germany, July 19–20, 2017, Proceedings
Editors	Einar Broch Johnsen
Pages	77-97
ISBN (Electronic)	978-3-319-61467-0
DOIs	https://doi.org/10.1007/978-3-319-61467-0_5
Publication status	Published - 18 Jun 2017
Event	11th International Conference on Tests & Proofs - Marburg, Germany Duration: 19 Jul 2017 → 20 Jul 2017 Conference number: 11 http://www.seceng.informatik.tu-darmstadt.de/tap2017/

Publication series

Name	Lecture notes in computer science
Publisher	Springer
Volume	10375

Conference

Conference	11th International Conference on Tests & Proofs
Abbreviated title	TAP 2017
Country/Territory	Germany
City	Marburg
Period	19/07/17 → 20/07/17
Internet address	http://www.seceng.informatik.tu-darmstadt.de/tap2017/

Keywords

2025 OA procedure

Access to Document

10.1007/978-3-319-61467-0_5

testingFinal published version, 762 KBLicence: Taverne

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Gerhold, M., & Stoelinga, M. (2017). Model-Based Testing of Probabilistic Systems with Stochastic Time. In E. Broch Johnsen (Ed.), Tests and Proofs: 11th International Conference, TAP 2017, Held as Part of STAF 2017, Marburg, Germany, July 19–20, 2017, Proceedings (pp. 77-97). (Lecture notes in computer science; Vol. 10375). https://doi.org/10.1007/978-3-319-61467-0_5

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title = "Model-Based Testing of Probabilistic Systems with Stochastic Time",

abstract = "This paper presents a model-based testing framework for black-box probabilistic systems with stochastic continuous time. Markov automata are used as an underlying model. We show how to generate, execute and evaluate test cases automatically from a probabilistically timed requirements model. In doing so, we connect classical ioco-theory with statistical hypothesis testing; our ioco-style algorithms test for functional behaviour, while χ2 hypothesis tests and confidence interval estimations assess the statistical correctness of the system. A crucial development are the classical soundness and completeness properties of our framework. Soundness states that test cases assign the correct verdict, while completeness states that our methods are powerful enough to discover each discrepancy in functional or statistical misbehaviour, up to arbitrary precision. We illustrate our framework via the Bluetooth device discovery protocol.",

keywords = "2025 OA procedure",

author = "Marcus Gerhold and Mari{\"e}lle Stoelinga",

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doi = "10.1007/978-3-319-61467-0\_5",

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series = "Lecture notes in computer science",

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editor = "\{Broch Johnsen\}, Einar",

booktitle = "Tests and Proofs",

note = "11th International Conference on Tests \& Proofs, TAP 2017 ; Conference date: 19-07-2017 Through 20-07-2017",

url = "http://www.seceng.informatik.tu-darmstadt.de/tap2017/",

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Gerhold, M & Stoelinga, M 2017, Model-Based Testing of Probabilistic Systems with Stochastic Time. in E Broch Johnsen (ed.), Tests and Proofs: 11th International Conference, TAP 2017, Held as Part of STAF 2017, Marburg, Germany, July 19–20, 2017, Proceedings. Lecture notes in computer science, vol. 10375, pp. 77-97, 11th International Conference on Tests & Proofs, Marburg, Germany, 19/07/17. https://doi.org/10.1007/978-3-319-61467-0_5

Model-Based Testing of Probabilistic Systems with Stochastic Time. / Gerhold, Marcus ; Stoelinga, Mariëlle.
Tests and Proofs: 11th International Conference, TAP 2017, Held as Part of STAF 2017, Marburg, Germany, July 19–20, 2017, Proceedings. ed. / Einar Broch Johnsen. 2017. p. 77-97 (Lecture notes in computer science; Vol. 10375).

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

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N2 - This paper presents a model-based testing framework for black-box probabilistic systems with stochastic continuous time. Markov automata are used as an underlying model. We show how to generate, execute and evaluate test cases automatically from a probabilistically timed requirements model. In doing so, we connect classical ioco-theory with statistical hypothesis testing; our ioco-style algorithms test for functional behaviour, while χ2 hypothesis tests and confidence interval estimations assess the statistical correctness of the system. A crucial development are the classical soundness and completeness properties of our framework. Soundness states that test cases assign the correct verdict, while completeness states that our methods are powerful enough to discover each discrepancy in functional or statistical misbehaviour, up to arbitrary precision. We illustrate our framework via the Bluetooth device discovery protocol.

AB - This paper presents a model-based testing framework for black-box probabilistic systems with stochastic continuous time. Markov automata are used as an underlying model. We show how to generate, execute and evaluate test cases automatically from a probabilistically timed requirements model. In doing so, we connect classical ioco-theory with statistical hypothesis testing; our ioco-style algorithms test for functional behaviour, while χ2 hypothesis tests and confidence interval estimations assess the statistical correctness of the system. A crucial development are the classical soundness and completeness properties of our framework. Soundness states that test cases assign the correct verdict, while completeness states that our methods are powerful enough to discover each discrepancy in functional or statistical misbehaviour, up to arbitrary precision. We illustrate our framework via the Bluetooth device discovery protocol.

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Model-Based Testing of Probabilistic Systems with Stochastic Time

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