80,404 research outputs found
Model checking Quantitative Linear Time Logic
This paper considers QLtl, a quantitative analagon of Ltl and presents algorithms for model checking QLtl over quantitative versions of Kripke structures and Markov chains
Counting LTL
The original publication is available at ieeexplore.ieee.org.International audienceThis paper presents a quantitative extension for the linear-time temporal logic LTL allowing to specify the number of states satisfying certain sub-formulas along paths. We give decision procedures for the satisfiability and model checking of this new temporal logic and study the complexity of the corresponding problems. Furthermore we show that the problems become undecidable when more expressive constraints are considered
Distributed Markovian Bisimulation Reduction aimed at CSL Model Checking
The verification of quantitative aspects like performance and dependability by means of model checking has become an important and vivid area of research over the past decade.\ud
\ud
An important result of that research is the logic CSL (continuous stochastic logic) and its corresponding model checking algorithms. The evaluation of properties expressed in CSL makes it necessary to solve large systems of linear (differential) equations, usually by means of numerical analysis. Both the inherent time and space complexity of the numerical algorithms make it practically infeasible to model check systems with more than 100 million states, whereas realistic system models may have billions of states.\ud
\ud
To overcome this severe restriction, it is important to be able to replace the original state space with a probabilistically equivalent, but smaller one. The most prominent equivalence relation is bisimulation, for which also a stochastic variant exists (Markovian bisimulation). In many cases, this bisimulation allows for a substantial reduction of the state space size. But, these savings in space come at the cost of an increased time complexity. Therefore in this paper a new distributed signature-based algorithm for the computation of the bisimulation quotient of a given state space is introduced.\ud
\ud
To demonstrate the feasibility of our approach in both a sequential, and more important, in a distributed setting, we have performed a number of case studies
Specifying and Monitoring Properties of Stochastic Spatio-Temporal Systems in Signal Temporal Logic
We present an extension of the linear time, time-bounded, Signal Temporal Logic to describe spatio-temporal properties. We consider a discrete location/ patch-based representation of space, with a population of interacting agents evolving in each location and with agents migrating from one patch to another one. We provide both a boolean and a quantitative semantics to this logic. We then present monitoring algorithms to check the validity of a formula, or to compute its satisfaction (robustness) score, over a spatio-temporal trace, exploiting these routines to do statistical model checking of stochastic models. We illustrate the logic at work on an epidemic example, looking at the diffusion of a cholera infection among communities living along a river
A Probabilistic Temporal Logic with Frequency Operators and Its Model Checking
Probabilistic Computation Tree Logic (PCTL) and Continuous Stochastic Logic
(CSL) are often used to describe specifications of probabilistic properties for
discrete time and continuous time, respectively. In PCTL and CSL, the
possibility of executions satisfying some temporal properties can be
quantitatively represented by the probabilistic extension of the path
quantifiers in their basic Computation Tree Logic (CTL), however, path formulae
of them are expressed via the same operators in CTL. For this reason, both of
them cannot represent formulae with quantitative temporal properties, such as
those of the form "some properties hold to more than 80% of time points (in a
certain bounded interval) on the path." In this paper, we introduce a new
temporal operator which expressed the notion of frequency of events, and define
probabilistic frequency temporal logic (PFTL) based on CTL\star. As a result,
we can easily represent the temporal properties of behavior in probabilistic
systems. However, it is difficult to develop a model checker for the full PFTL,
due to rich expressiveness. Accordingly, we develop a model-checking algorithm
for the CTL-like fragment of PFTL against finite-state Markov chains, and an
approximate model-checking algorithm for the bounded Linear Temporal Logic
(LTL) -like fragment of PFTL against countable-state Markov chains.Comment: In Proceedings INFINITY 2011, arXiv:1111.267
A decidable policy language for history-based transaction monitoring
Online trading invariably involves dealings between strangers, so it is
important for one party to be able to judge objectively the trustworthiness of
the other. In such a setting, the decision to trust a user may sensibly be
based on that user's past behaviour. We introduce a specification language
based on linear temporal logic for expressing a policy for categorising the
behaviour patterns of a user depending on its transaction history. We also
present an algorithm for checking whether the transaction history obeys the
stated policy. To be useful in a real setting, such a language should allow one
to express realistic policies which may involve parameter quantification and
quantitative or statistical patterns. We introduce several extensions of linear
temporal logic to cater for such needs: a restricted form of universal and
existential quantification; arbitrary computable functions and relations in the
term language; and a "counting" quantifier for counting how many times a
formula holds in the past. We then show that model checking a transaction
history against a policy, which we call the history-based transaction
monitoring problem, is PSPACE-complete in the size of the policy formula and
the length of the history. The problem becomes decidable in polynomial time
when the policies are fixed. We also consider the problem of transaction
monitoring in the case where not all the parameters of actions are observable.
We formulate two such "partial observability" monitoring problems, and show
their decidability under certain restrictions
Formal Verification of Probabilistic SystemC Models with Statistical Model Checking
Transaction-level modeling with SystemC has been very successful in
describing the behavior of embedded systems by providing high-level executable
models, in which many of them have inherent probabilistic behaviors, e.g.,
random data and unreliable components. It thus is crucial to have both
quantitative and qualitative analysis of the probabilities of system
properties. Such analysis can be conducted by constructing a formal model of
the system under verification and using Probabilistic Model Checking (PMC).
However, this method is infeasible for large systems, due to the state space
explosion. In this article, we demonstrate the successful use of Statistical
Model Checking (SMC) to carry out such analysis directly from large SystemC
models and allow designers to express a wide range of useful properties. The
first contribution of this work is a framework to verify properties expressed
in Bounded Linear Temporal Logic (BLTL) for SystemC models with both timed and
probabilistic characteristics. Second, the framework allows users to expose a
rich set of user-code primitives as atomic propositions in BLTL. Moreover,
users can define their own fine-grained time resolution rather than the
boundary of clock cycles in the SystemC simulation. The third contribution is
an implementation of a statistical model checker. It contains an automatic
monitor generation for producing execution traces of the
model-under-verification (MUV), the mechanism for automatically instrumenting
the MUV, and the interaction with statistical model checking algorithms.Comment: Journal of Software: Evolution and Process. Wiley, 2017. arXiv admin
note: substantial text overlap with arXiv:1507.0818
- …