Formal Verification of Fault Tolerance Using Theorem-Proving Techniques
IEEE Transactions on Computers
The essence of functional programming
POPL '92 Proceedings of the 19th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Formal Verification for Fault-Tolerant Architectures: Prolegomena to the Design of PVS
IEEE Transactions on Software Engineering
PRISM: Probabilistic Symbolic Model Checker
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Formal methods for the validation of fault tolerance in autonomous spacecraft
FTCS '96 Proceedings of the The Twenty-Sixth Annual International Symposium on Fault-Tolerant Computing (FTCS '96)
DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
Abstraction, Refinement And Proof For Probabilistic Systems (Monographs in Computer Science)
Abstraction, Refinement And Proof For Probabilistic Systems (Monographs in Computer Science)
Proofs of randomized algorithms in Coq
Science of Computer Programming
Patterns for Fault Tolerant Software
Patterns for Fault Tolerant Software
FM '09 Proceedings of the 2nd World Congress on Formal Methods
Qualitative probabilistic modelling in event-B
IFM'07 Proceedings of the 6th international conference on Integrated formal methods
Proving the security of ElGamal encryption via indistinguishability logic
Proceedings of the 2011 ACM Symposium on Applied Computing
Generating Invariant-Based Certificates for Embedded Systems
ACM Transactions on Embedded Computing Systems (TECS)
Hi-index | 0.00 |
We present a framework to formally describe system behavior and symbolically reason about possible failures. We regard systems which are composed of different units: sensors, computational parts and actuators. Considering worst-case failure behavior of system components, our framework is used to derive reliability guarantees for composed systems. The behavior of system components is modeled using monad like constructs that serve as an abstract representation for system behavior. We introduce rules to reason about these representations and derive results like, e.g., guaranteed upper bounds for system failure. Our approach is characterized by the fact that we do not just map a certain component to a failure probability, but regard distributions of error behavior. These serve as basis for deriving failure probabilities.