On Incremental Quantitative Verification for Probabilistic Systems

13 pages•Published: February 12, 2014

Marta Kwiatkowska, David Parker, Hongyang Qu and Mateusz Ujma

Abstract

Quantitative verification techniques offer an effective means
of computing performance and reliability properties for a wide range of systems.
In many cases, it is necessary to perform repeated analyses of a system,
for example to identify trends in results, determine optimal system parameters
or when performing online analysis for adaptive systems.
We argue the need for incremental quantitative verification techniques
which are able to re-use results from previous verification runs in order to improve efficiency.
We report on recently proposed techniques for incremental quantitative verification of Markov decision processes,
based on a decomposition of the model into its strongly connected components.
We give an overview of the method, describe a number of useful optimisations
and show experimental results that illustrate significant gains in run-time performance
using the incremental approach.

In: Andrei Voronkov and Margarita Korovina (editors). HOWARD-60. A Festschrift on the Occasion of Howard Barringer's 60th Birthday, vol 42, pages 245-257.

Links:	https://easychair.org/publications/paper/B3Nw
	https://doi.org/10.29007/bmcf

BibTeX entry

@inproceedings{HOWARD-60:Incremental_Quantitative_Verification_Probabilistic,
  author    = {Marta Kwiatkowska and David Parker and Hongyang Qu and Mateusz Ujma},
  title     = {On Incremental Quantitative Verification for Probabilistic Systems},
  booktitle = {HOWARD-60. A Festschrift on the Occasion of Howard Barringer's 60th Birthday},
  editor    = {Andrei Voronkov and Margarita Korovina},
  series    = {EPiC Series in Computing},
  volume    = {42},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/B3Nw},
  doi       = {10.29007/bmcf},
  pages     = {245-257},
  year      = {2014}}

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