Dependability analysis and verification for CONNECTed systems
ISoLA'10 Proceedings of the 4th international conference on Leveraging applications of formal methods, verification, and validation - Volume Part II
Quantitative multi-objective verification for probabilistic systems
TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
Automated learning of probabilistic assumptions for compositional reasoning
FASE'11/ETAPS'11 Proceedings of the 14th international conference on Fundamental approaches to software engineering: part of the joint European conferences on theory and practice of software
Learning-based compositional verification for synchronous probabilistic systems
ATVA'11 Proceedings of the 9th international conference on Automated technology for verification and analysis
From software verification to `everyware' verification
Computer Science - Research and Development
Compositional probabilistic verification through multi-objective model checking
Information and Computation
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We present a fully automated technique for compositional verification of probabilistic systems. Our approach builds upon a recently proposed assume-guarantee framework for probabilistic automata, in which assumptions and guarantees are probabilistic safety properties, represented using finite automata. A limitation of this work is that the assumptions need to be created manually. To overcome this, we propose a novel learning technique based on the L* algorithm, which automatically generates probabilistic assumptions using the results of queries executed by a probabilistic model checker. Learnt assumptions either establish satisfaction of the verification problem or are used to generate a probabilistic counterexample that refutes it. In the case where an assumption cannot be generated, lower and upper bounds on the probability of satisfaction are produced. We illustrate the applicability of the approach on a range of case studies.