64,126 research outputs found
Synthesis of Parametric Programs using Genetic Programming and Model Checking
Formal methods apply algorithms based on mathematical principles to enhance
the reliability of systems. It would only be natural to try to progress from
verification, model checking or testing a system against its formal
specification into constructing it automatically. Classical algorithmic
synthesis theory provides interesting algorithms but also alarming high
complexity and undecidability results. The use of genetic programming, in
combination with model checking and testing, provides a powerful heuristic to
synthesize programs. The method is not completely automatic, as it is fine
tuned by a user that sets up the specification and parameters. It also does not
guarantee to always succeed and converge towards a solution that satisfies all
the required properties. However, we applied it successfully on quite
nontrivial examples and managed to find solutions to hard programming
challenges, as well as to improve and to correct code. We describe here several
versions of our method for synthesizing sequential and concurrent systems.Comment: In Proceedings INFINITY 2013, arXiv:1402.661
Using parametric set constraints for locating errors in CLP programs
This paper introduces a framework of parametric descriptive directional types
for constraint logic programming (CLP). It proposes a method for locating type
errors in CLP programs and presents a prototype debugging tool. The main
technique used is checking correctness of programs w.r.t. type specifications.
The approach is based on a generalization of known methods for proving
correctness of logic programs to the case of parametric specifications.
Set-constraint techniques are used for formulating and checking verification
conditions for (parametric) polymorphic type specifications. The specifications
are expressed in a parametric extension of the formalism of term grammars. The
soundness of the method is proved and the prototype debugging tool supporting
the proposed approach is illustrated on examples.
The paper is a substantial extension of the previous work by the same authors
concerning monomorphic directional types.Comment: 64 pages, To appear in Theory and Practice of Logic Programmin
Learning and Designing Stochastic Processes from Logical Constraints
Stochastic processes offer a flexible mathematical formalism to model and
reason about systems. Most analysis tools, however, start from the premises
that models are fully specified, so that any parameters controlling the
system's dynamics must be known exactly. As this is seldom the case, many
methods have been devised over the last decade to infer (learn) such parameters
from observations of the state of the system. In this paper, we depart from
this approach by assuming that our observations are {\it qualitative}
properties encoded as satisfaction of linear temporal logic formulae, as
opposed to quantitative observations of the state of the system. An important
feature of this approach is that it unifies naturally the system identification
and the system design problems, where the properties, instead of observations,
represent requirements to be satisfied. We develop a principled statistical
estimation procedure based on maximising the likelihood of the system's
parameters, using recent ideas from statistical machine learning. We
demonstrate the efficacy and broad applicability of our method on a range of
simple but non-trivial examples, including rumour spreading in social networks
and hybrid models of gene regulation
IMITATOR II: A Tool for Solving the Good Parameters Problem in Timed Automata
We present here Imitator II, a new version of Imitator, a tool implementing
the "inverse method" for parametric timed automata: given a reference valuation
of the parameters, it synthesizes a constraint such that, for any valuation
satisfying this constraint, the system behaves the same as under the reference
valuation in terms of traces, i.e., alternating sequences of locations and
actions. Imitator II also implements the "behavioral cartography algorithm",
allowing us to solve the following good parameters problem: find a set of
valuations within a given bounded parametric domain for which the system
behaves well. We present new features and optimizations of the tool, and give
results of applications to various examples of asynchronous circuits and
communication protocols.Comment: In Proceedings INFINITY 2010, arXiv:1010.611
Parametric Schedulability Analysis of Fixed Priority Real-Time Distributed Systems
Parametric analysis is a powerful tool for designing modern embedded systems,
because it permits to explore the space of design parameters, and to check the
robustness of the system with respect to variations of some uncontrollable
variable. In this paper, we address the problem of parametric schedulability
analysis of distributed real-time systems scheduled by fixed priority. In
particular, we propose two different approaches to parametric analysis: the
first one is a novel technique based on classical schedulability analysis,
whereas the second approach is based on model checking of Parametric Timed
Automata (PTA).
The proposed analytic method extends existing sensitivity analysis for single
processors to the case of a distributed system, supporting preemptive and
non-preemptive scheduling, jitters and unconstrained deadlines. Parametric
Timed Automata are used to model all possible behaviours of a distributed
system, and therefore it is a necessary and sufficient analysis. Both
techniques have been implemented in two software tools, and they have been
compared with classical holistic analysis on two meaningful test cases. The
results show that the analytic method provides results similar to classical
holistic analysis in a very efficient way, whereas the PTA approach is slower
but covers the entire space of solutions.Comment: Submitted to ECRTS 2013 (http://ecrts.eit.uni-kl.de/ecrts13
Non-parametric Bayesian modeling of complex networks
Modeling structure in complex networks using Bayesian non-parametrics makes
it possible to specify flexible model structures and infer the adequate model
complexity from the observed data. This paper provides a gentle introduction to
non-parametric Bayesian modeling of complex networks: Using an infinite mixture
model as running example we go through the steps of deriving the model as an
infinite limit of a finite parametric model, inferring the model parameters by
Markov chain Monte Carlo, and checking the model's fit and predictive
performance. We explain how advanced non-parametric models for complex networks
can be derived and point out relevant literature
Comment: Bayesian Checking of the Second Levels of Hierarchical Models
Comment: Bayesian Checking of the Second Levels of Hierarchical Models
[arXiv:0802.0743]Comment: Published in at http://dx.doi.org/10.1214/07-STS235A the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Workshop on Verification and Theorem Proving for Continuous Systems (NetCA Workshop 2005)
Oxford, UK, 26 August 200
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