On incremental quantitative verification for probabilistic systems

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.