### Abstract

Original language | Undefined |
---|---|

Title of host publication | Proceedings of the Second International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (SETTA 2016) |

Place of Publication | London |

Publisher | Springer Verlag |

Pages | 85-100 |

Number of pages | 16 |

ISBN (Print) | 978-3-319-47676-6 |

DOIs | |

State | Published - Nov 2016 |

### Publication series

Name | Lecture Notes in Computer Science |
---|---|

Publisher | Springer Verlag |

Volume | 9984 |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Fingerprint

### Keywords

- EWI-27541
- METIS-320924
- IR-103039

### Cite this

*Proceedings of the Second International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (SETTA 2016)*(pp. 85-100). (Lecture Notes in Computer Science; Vol. 9984). London: Springer Verlag. DOI: 10.1007/978-3-319-47677-3_6

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*Proceedings of the Second International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (SETTA 2016).*Lecture Notes in Computer Science, vol. 9984, Springer Verlag, London, pp. 85-100. DOI: 10.1007/978-3-319-47677-3_6

**A Comparison of Time- and Reward-Bounded Probabilistic Model Checking Techniques.** / Hahn, Ernst Moritz; Hartmanns, Arnd.

Research output: Scientific - peer-review › Conference contribution

TY - CHAP

T1 - A Comparison of Time- and Reward-Bounded Probabilistic Model Checking Techniques

AU - Hahn,Ernst Moritz

AU - Hartmanns,Arnd

N1 - eemcs-eprint-27541

PY - 2016/11

Y1 - 2016/11

N2 - In the design of probabilistic timed systems, requirements concerning behaviour that occurs within a given time or energy budget are of central importance. We observe that model-checking such requirements for probabilistic timed automata can be reduced to checking reward-bounded properties on Markov decision processes. This is traditionally implemented by unfolding the model according to the bound, or by solving a sequence of linear programs. Neither scales well to large models. Using value iteration in place of linear programming achieves scalability but accumulates approximation error. In this paper, we correct the value iteration-based scheme, present two new approaches based on scheduler enumeration and state elimination, and compare the practical performance and scalability of all techniques on a number of case studies from the literature. We show that state elimination can significantly reduce runtime for large models or high bounds.

AB - In the design of probabilistic timed systems, requirements concerning behaviour that occurs within a given time or energy budget are of central importance. We observe that model-checking such requirements for probabilistic timed automata can be reduced to checking reward-bounded properties on Markov decision processes. This is traditionally implemented by unfolding the model according to the bound, or by solving a sequence of linear programs. Neither scales well to large models. Using value iteration in place of linear programming achieves scalability but accumulates approximation error. In this paper, we correct the value iteration-based scheme, present two new approaches based on scheduler enumeration and state elimination, and compare the practical performance and scalability of all techniques on a number of case studies from the literature. We show that state elimination can significantly reduce runtime for large models or high bounds.

KW - EWI-27541

KW - METIS-320924

KW - IR-103039

U2 - 10.1007/978-3-319-47677-3_6

DO - 10.1007/978-3-319-47677-3_6

M3 - Conference contribution

SN - 978-3-319-47676-6

T3 - Lecture Notes in Computer Science

SP - 85

EP - 100

BT - Proceedings of the Second International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (SETTA 2016)

PB - Springer Verlag

ER -