Model checking
Quantitative solution of omega-regular games380872
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Quantitative stochastic parity games
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Quantitative verification: models techniques and tools
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Runtime software adaptation: framework, approaches, and styles
Companion of the 30th international conference on Software engineering
Using quantitative analysis to implement autonomic IT systems
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
CrystalBall: predicting and preventing inconsistencies in deployed distributed systems
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
Software Engineering for Self-Adaptive Systems: A Research Roadmap
Software Engineering for Self-Adaptive Systems
Discounting the future in systems theory
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Run-time efficient probabilistic model checking
Proceedings of the 33rd International Conference on Software Engineering
PRISM 4.0: verification of probabilistic real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Incremental quantitative verification for Markov decision processes
DSN '11 Proceedings of the 2011 IEEE/IFIP 41st International Conference on Dependable Systems&Networks
The complexity of stochastic rabin and streett games
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
A survey of stochastic ω-regular games
Journal of Computer and System Sciences
Gist: a solver for probabilistic games
CAV'10 Proceedings of the 22nd international conference on Computer Aided Verification
A formal approach to adaptive software: continuous assurance of non-functional requirements
Formal Aspects of Computing
Large-scale complex IT systems
Communications of the ACM
Automatic verification of competitive stochastic systems
TACAS'12 Proceedings of the 18th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Self-adaptive software needs quantitative verification at runtime
Communications of the ACM
Runtime verification with state estimation
RV'11 Proceedings of the Second international conference on Runtime verification
Partial-Observation Stochastic Games: How to Win When Belief Fails
LICS '12 Proceedings of the 2012 27th Annual IEEE/ACM Symposium on Logic in Computer Science
Pareto curves for probabilistic model checking
ATVA'12 Proceedings of the 10th international conference on Automated Technology for Verification and Analysis
Verification of partial-information probabilistic systems using counterexample-guided refinements
ATVA'12 Proceedings of the 10th international conference on Automated Technology for Verification and Analysis
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Quantitative verification is an established automated technique that can ensure predictability and dependability of software systems which exhibit probabilistic behaviour. Since offline usage of quantitative verification is infeasible for large-scale complex systems that continuously adapt to the changing environment, quantitative runtime verification was proposed as an alternative. Using an illustrative case study of communicating, distributed probabilistic processes, we formulate the problem of quantitative steering, a runtime technique that involves system monitoring, prediction of future errors, and enforcement of system's behaviour away from the error states. We consider a communication-based variant of steering where enforcement is achieved by modifying the contents of communication channels. Our approach is based on stochastic games, where one player is the system and the other players assume the role of the controller, and hence steering reduces to finding a controller strategy that meets the given quantitative goal. We discuss the solution to the quantitative steering problem and its extensions inspired by complex real-world scenarios.