Hierarchical correctness proofs for distributed algorithms
PODC '87 Proceedings of the sixth annual ACM Symposium on Principles of distributed computing
Composition and behaviors of probabilistic I/O automata
Theoretical Computer Science
Model checking
Communicating sequential processes
Communications of the ACM
Formal verification of parallel programs
Communications of the ACM
Proceedings of the 8th European software engineering conference held jointly with 9th ACM SIGSOFT international symposium on Foundations of software engineering
Operational Profiles in Software-Reliability Engineering
IEEE Software
Testing Equivalences and Fully Abstract Models for Probabilistic Processes
CONCUR '90 Proceedings of the Theories of Concurrency: Unification and Extension
Probabilistic Simulations for Probabilistic Processes
CONCUR '94 Proceedings of the Concurrency Theory
A User-Oriented Software Reliability Model
IEEE Transactions on Software Engineering
Model evolution by run-time parameter adaptation
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
PRISM: a tool for automatic verification of probabilistic systems
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Probabilistic automata for architecture-based reliability assessment
Proceedings of the 2010 ICSE Workshop on Quantitative Stochastic Models in the Verification and Design of Software Systems
My model checker died!: how well did it do?
Proceedings of the 2010 ICSE Workshop on Quantitative Stochastic Models in the Verification and Design of Software Systems
Assume-Guarantee verification for probabilistic systems
TACAS'10 Proceedings of the 16th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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System specifications have long been expressed through automata-based languages, enabling verification techniques such as model checking. These verification techniques can assess whether a property holds or not, given a system specification. Quantitative model checking can provide additional information on the probability of these properties holding. We are interested in quantitatively analysing the probability of errors in non-probabilistic system models by composing them with probabilistic models of the environment. Although many probabilistic automata-based formalisms and composition operators exist, these are not adequate for such a setting. In this work we present a formalism inspired on interface automata and a suitable composition operator for these automata that enables validation of environment models in isolation and sound analysis of its composition with the non-probabilistic model of the system-under-analysis.