When the requirements for adaptation and high integrity meet
Proceedings of the 8th workshop on Assurances for self-adaptive systems
QoS verification and model tuning @ runtime
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Self-adaptive software needs quantitative verification at runtime
Communications of the ACM
Improving GPU sparse matrix-vector multiplication for probabilistic model checking
SPIN'12 Proceedings of the 19th international conference on Model Checking Software
Towards communication-based steering of complex distributed systems
Proceedings of the 17th Monterey conference on Large-Scale Complex IT Systems: development, operation and management
An incremental verification framework for component-based software systems
Proceedings of the 16th International ACM Sigsoft symposium on Component-based software engineering
From software verification to `everyware' verification
Computer Science - Research and Development
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Quantitative verification techniques provide an effective means of computing performance and reliability properties for a wide range of systems. However, the computation required can be expensive, particularly if it has to be performed multiple times, for example to determine optimal system parameters. We present efficient incremental techniques for quantitative verification of Markov decision processes, which are able to re-use results from previous verification runs, based on a decomposition of the model into its strongly connected components (SCCs). We also show how this SCC-based approach can be further optimised to improve verification speed and how it can be combined with symbolic data structures to offer better scalability. We illustrate the effectiveness of the approach on a selection of large case studies.