1,252 research outputs found
Abstractions of stochastic hybrid systems
Many control systems have large, infinite state space that can not be easily abstracted. One method to analyse and verify these systems is reachability analysis. It is frequently used for air traffic control and power plants. Because of lack of complete information about the environment or unpredicted changes, the stochastic approach is a viable alternative. In this paper, different ways of introducing rechability under uncertainty are presented. A new concept of stochastic bisimulation is introduced and its connection with the reachability analysis is established. The work is mainly motivated by safety critical situations in air traffic control (like collision detection and avoidance) and formal tools are based on stochastic analysis
Verification of Confidentiality of Multi-threaded Programs
An introduction of Slalom project: motivation, plans and some result
Model Checking Dynamic-Epistemic Spatial Logic
In this paper we focus on Dynamic Spatial Logic, the extension of Hennessy-Milner logic with the parallel operator. We develop a sound complete Hilbert-style axiomatic system for it comprehending the behavior of spatial operators in relation with dynamic/temporal ones. Underpining on a new congruence we define over the class of processes - the structural bisimulation - we prove the finite model property for this logic that provides the decidability for satisfiability, validity and model checking against process semantics. Eventualy we propose algorithms for validity, satisfiability and model checking
Abstraction and Learning for Infinite-State Compositional Verification
Despite many advances that enable the application of model checking
techniques to the verification of large systems, the state-explosion problem
remains the main challenge for scalability. Compositional verification
addresses this challenge by decomposing the verification of a large system into
the verification of its components. Recent techniques use learning-based
approaches to automate compositional verification based on the assume-guarantee
style reasoning. However, these techniques are only applicable to finite-state
systems. In this work, we propose a new framework that interleaves abstraction
and learning to perform automated compositional verification of infinite-state
systems. We also discuss the role of learning and abstraction in the related
context of interface generation for infinite-state components.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455
Process Algebras
Process Algebras are mathematically rigorous languages with well defined semantics that permit describing and verifying properties of concurrent communicating systems.
They can be seen as models of processes, regarded as agents that act and interact continuously with other similar agents and with their common environment. The agents may be real-world objects (even people), or they may be artifacts, embodied perhaps in computer hardware or software systems.
Many different approaches (operational, denotational, algebraic) are taken for describing the meaning of processes. However, the operational approach is the reference one. By relying on the so called Structural Operational Semantics (SOS), labelled transition systems are built and composed by using the different operators of the many different process algebras. Behavioral equivalences are used to abstract from unwanted details and identify those systems that react similarly to external
experiments
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