8,414 research outputs found
Under-approximating Cut Sets for Reachability in Large Scale Automata Networks
In the scope of discrete finite-state models of interacting components, we
present a novel algorithm for identifying sets of local states of components
whose activity is necessary for the reachability of a given local state. If all
the local states from such a set are disabled in the model, the concerned
reachability is impossible. Those sets are referred to as cut sets and are
computed from a particular abstract causality structure, so-called Graph of
Local Causality, inspired from previous work and generalised here to finite
automata networks. The extracted sets of local states form an
under-approximation of the complete minimal cut sets of the dynamics: there may
exist smaller or additional cut sets for the given reachability. Applied to
qualitative models of biological systems, such cut sets provide potential
therapeutic targets that are proven to prevent molecules of interest to become
active, up to the correctness of the model. Our new method makes tractable the
formal analysis of very large scale networks, as illustrated by the computation
of cut sets within a Boolean model of biological pathways interactions
gathering more than 9000 components
Verifying Recursive Active Documents with Positive Data Tree Rewriting
This paper proposes a data tree-rewriting framework for modeling evolving
documents. The framework is close to Guarded Active XML, a platform used for
handling XML repositories evolving through web services. We focus on automatic
verification of properties of evolving documents that can contain data from an
infinite domain. We establish the boundaries of decidability, and show that
verification of a {\em positive} fragment that can handle recursive service
calls is decidable. We also consider bounded model-checking in our data
tree-rewriting framework and show that it is \nexptime-complete
Efficient Parallel Statistical Model Checking of Biochemical Networks
We consider the problem of verifying stochastic models of biochemical
networks against behavioral properties expressed in temporal logic terms. Exact
probabilistic verification approaches such as, for example, CSL/PCTL model
checking, are undermined by a huge computational demand which rule them out for
most real case studies. Less demanding approaches, such as statistical model
checking, estimate the likelihood that a property is satisfied by sampling
executions out of the stochastic model. We propose a methodology for
efficiently estimating the likelihood that a LTL property P holds of a
stochastic model of a biochemical network. As with other statistical
verification techniques, the methodology we propose uses a stochastic
simulation algorithm for generating execution samples, however there are three
key aspects that improve the efficiency: first, the sample generation is driven
by on-the-fly verification of P which results in optimal overall simulation
time. Second, the confidence interval estimation for the probability of P to
hold is based on an efficient variant of the Wilson method which ensures a
faster convergence. Third, the whole methodology is designed according to a
parallel fashion and a prototype software tool has been implemented that
performs the sampling/verification process in parallel over an HPC
architecture
MCMAS-SLK: A Model Checker for the Verification of Strategy Logic Specifications
We introduce MCMAS-SLK, a BDD-based model checker for the verification of
systems against specifications expressed in a novel, epistemic variant of
strategy logic. We give syntax and semantics of the specification language and
introduce a labelling algorithm for epistemic and strategy logic modalities. We
provide details of the checker which can also be used for synthesising agents'
strategies so that a specification is satisfied by the system. We evaluate the
efficiency of the implementation by discussing the results obtained for the
dining cryptographers protocol and a variant of the cake-cutting problem
Probabilistic Model Checking for Energy Analysis in Software Product Lines
In a software product line (SPL), a collection of software products is
defined by their commonalities in terms of features rather than explicitly
specifying all products one-by-one. Several verification techniques were
adapted to establish temporal properties of SPLs. Symbolic and family-based
model checking have been proven to be successful for tackling the combinatorial
blow-up arising when reasoning about several feature combinations. However,
most formal verification approaches for SPLs presented in the literature focus
on the static SPLs, where the features of a product are fixed and cannot be
changed during runtime. This is in contrast to dynamic SPLs, allowing to adapt
feature combinations of a product dynamically after deployment. The main
contribution of the paper is a compositional modeling framework for dynamic
SPLs, which supports probabilistic and nondeterministic choices and allows for
quantitative analysis. We specify the feature changes during runtime within an
automata-based coordination component, enabling to reason over strategies how
to trigger dynamic feature changes for optimizing various quantitative
objectives, e.g., energy or monetary costs and reliability. For our framework
there is a natural and conceptually simple translation into the input language
of the prominent probabilistic model checker PRISM. This facilitates the
application of PRISM's powerful symbolic engine to the operational behavior of
dynamic SPLs and their family-based analysis against various quantitative
queries. We demonstrate feasibility of our approach by a case study issuing an
energy-aware bonding network device.Comment: 14 pages, 11 figure
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