132,770 research outputs found
Compositional Verification and Optimization of Interactive Markov Chains
Interactive Markov chains (IMC) are compositional behavioural models
extending labelled transition systems and continuous-time Markov chains. We
provide a framework and algorithms for compositional verification and
optimization of IMC with respect to time-bounded properties. Firstly, we give a
specification formalism for IMC. Secondly, given a time-bounded property, an
IMC component and the assumption that its unknown environment satisfies a given
specification, we synthesize a scheduler for the component optimizing the
probability that the property is satisfied in any such environment
Progression and Verification of Situation Calculus Agents with Bounded Beliefs
We investigate agents that have incomplete information and make decisions based on their beliefs expressed as situation calculus bounded action theories. Such theories have an infinite object domain, but the number of objects that belong to fluents at each time point is bounded by a given constant. Recently, it has been shown that verifying temporal properties over such theories is decidable. We take a first-person view and use the theory to capture what the agent believes about the domain of interest and the actions affecting it. In this paper, we study verification of temporal properties over online executions. These are executions resulting from agents performing only actions that are feasible according to their beliefs. To do so, we first examine progression, which captures belief state update resulting from actions in the situation calculus. We show that, for bounded action theories, progression, and hence belief states, can always be represented as a bounded first-order logic theory. Then, based on this result, we prove decidability of temporal verification over online executions for bounded action theories. © 2015 The Author(s
Simulator Semantics for System Level Formal Verification
Many simulation based Bounded Model Checking approaches to System Level
Formal Verification (SLFV) have been devised. Typically such approaches exploit
the capability of simulators to save computation time by saving and restoring
the state of the system under simulation. However, even though such approaches
aim to (bounded) formal verification, as a matter of fact, the simulator
behaviour is not formally modelled and the proof of correctness of the proposed
approaches basically relies on the intuitive notion of simulator behaviour.
This gap makes it hard to check if the optimisations introduced to speed up the
simulation do not actually omit checking relevant behaviours of the system
under verification.
The aim of this paper is to fill the above gap by presenting a formal
semantics for simulators.Comment: In Proceedings GandALF 2015, arXiv:1509.0685
Incremental bounded model checking for embedded software
Program analysis is on the brink of mainstream usage in embedded systems development. Formal verification of behavioural requirements, finding runtime errors and test case generation are some of the most common applications of automated verification tools based on bounded model checking (BMC). Existing industrial tools for embedded software use an off-the-shelf bounded model checker and apply it iteratively to verify the program with an increasing number of unwindings. This approach unnecessarily wastes time repeating work that has already been done and fails to exploit the power of incremental SAT solving. This article reports on the extension of the software model checker CBMC to support incremental BMC and its successful integration with the industrial embedded software verification tool BTC EMBEDDED TESTER. We present an extensive evaluation over large industrial embedded programs, mainly from the automotive industry. We show that incremental BMC cuts runtimes by one order of magnitude in comparison to the standard non-incremental approach, enabling the application of formal verification to large and complex embedded software. We furthermore report promising results on analysing programs with arbitrary loop structure using incremental BMC, demonstrating its applicability and potential to verify general software beyond the embedded domain
Data-driven and Model-based Verification: a Bayesian Identification Approach
This work develops a measurement-driven and model-based formal verification
approach, applicable to systems with partly unknown dynamics. We provide a
principled method, grounded on reachability analysis and on Bayesian inference,
to compute the confidence that a physical system driven by external inputs and
accessed under noisy measurements, verifies a temporal logic property. A case
study is discussed, where we investigate the bounded- and unbounded-time safety
of a partly unknown linear time invariant system
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