2014
DOI: 10.1007/978-3-319-10696-0_31
|View full text |Cite
|
Sign up to set email alerts
|

Accelerating Parametric Probabilistic Verification

Abstract: We present a novel method for computing reachability probabilities of parametric discrete-time Markov chains whose transition probabilities are fractions of polynomials over a set of parameters. Our algorithm is based on two key ingredients: a graph decomposition into strongly connected subgraphs combined with a novel factorization strategy for polynomials. Experimental evaluations show that these approaches can lead to a speed-up of up to several orders of magnitude in comparison to existing approaches. Preli… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
57
0

Year Published

2019
2019
2021
2021

Publication Types

Select...
5
1

Relationship

2
4

Authors

Journals

citations
Cited by 51 publications
(57 citation statements)
references
References 25 publications
0
57
0
Order By: Relevance
“…A parametric Markov chain is an MC comprising transition probabilities P(s, s ) and/or rewards ρ(s) defined as rational functions over a set of continuous variables [20], [34], [36].…”
Section: Definitionmentioning
confidence: 99%
See 2 more Smart Citations
“…A parametric Markov chain is an MC comprising transition probabilities P(s, s ) and/or rewards ρ(s) defined as rational functions over a set of continuous variables [20], [34], [36].…”
Section: Definitionmentioning
confidence: 99%
“…Parametric model checking (PMC) [20], [34], [36] is a formal technique for the analysis of Markov chains with transitions probabilities specified as rational functions over a set of continuous variables. When the analysed Markov chains model software systems, these variables represent configurable parameters of the software or environment parameters unknown until runtime.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…If assumptions are needed (l. [21][22][23][24][25][26], then we push the three assumptions to the Queue. Therefore, l. 27, should not be executed, so we continue to the next iteration of the while loop after adding assumptions (l. 26) At the end, we check for global monotonicity (l. [29][30][31][32][33][34][35][36][37].…”
Section: B Full Algorithmmentioning
confidence: 99%
“…This method contracts each SCC into a set of states, one for each entry state of the SCC. Applied to pMCs [31], it preserves the reachability probabilities of target set T . For each SCC, all nonentry states (i.e., states without incoming transitions from outside the SCC) are eliminated by state elimination [18,27] and transitions between entry states are deleted [19].…”
Section: B Full Algorithmmentioning
confidence: 99%