3,427 research outputs found
Sampled Semantics of Timed Automata
Sampled semantics of timed automata is a finite approximation of their dense
time behavior. While the former is closer to the actual software or hardware
systems with a fixed granularity of time, the abstract character of the latter
makes it appealing for system modeling and verification. We study one aspect of
the relation between these two semantics, namely checking whether the system
exhibits some qualitative (untimed) behaviors in the dense time which cannot be
reproduced by any implementation with a fixed sampling rate. More formally, the
\emph{sampling problem} is to decide whether there is a sampling rate such that
all qualitative behaviors (the untimed language) accepted by a given timed
automaton in dense time semantics can be also accepted in sampled semantics. We
show that this problem is decidable
A Theory of Sampling for Continuous-time Metric Temporal Logic
This paper revisits the classical notion of sampling in the setting of
real-time temporal logics for the modeling and analysis of systems. The
relationship between the satisfiability of Metric Temporal Logic (MTL) formulas
over continuous-time models and over discrete-time models is studied. It is
shown to what extent discrete-time sequences obtained by sampling
continuous-time signals capture the semantics of MTL formulas over the two time
domains. The main results apply to "flat" formulas that do not nest temporal
operators and can be applied to the problem of reducing the verification
problem for MTL over continuous-time models to the same problem over
discrete-time, resulting in an automated partial practically-efficient
discretization technique.Comment: Revised version, 43 pages
An Efficient Algorithm for Monitoring Practical TPTL Specifications
We provide a dynamic programming algorithm for the monitoring of a fragment
of Timed Propositional Temporal Logic (TPTL) specifications. This fragment of
TPTL, which is more expressive than Metric Temporal Logic, is characterized by
independent time variables which enable the elicitation of complex real-time
requirements. For this fragment, we provide an efficient polynomial time
algorithm for off-line monitoring of finite traces. Finally, we provide
experimental results on a prototype implementation of our tool in order to
demonstrate the feasibility of using our tool in practical applications
A Hierarchy of Scheduler Classes for Stochastic Automata
Stochastic automata are a formal compositional model for concurrent
stochastic timed systems, with general distributions and non-deterministic
choices. Measures of interest are defined over schedulers that resolve the
nondeterminism. In this paper we investigate the power of various theoretically
and practically motivated classes of schedulers, considering the classic
complete-information view and a restriction to non-prophetic schedulers. We
prove a hierarchy of scheduler classes w.r.t. unbounded probabilistic
reachability. We find that, unlike Markovian formalisms, stochastic automata
distinguish most classes even in this basic setting. Verification and strategy
synthesis methods thus face a tradeoff between powerful and efficient classes.
Using lightweight scheduler sampling, we explore this tradeoff and demonstrate
the concept of a useful approximative verification technique for stochastic
automata
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
Input Synthesis for Sampled Data Systems by Program Logic
Inspired by a concrete industry problem we consider the input synthesis
problem for hybrid systems: given a hybrid system that is subject to input from
outside (also called disturbance or noise), find an input sequence that steers
the system to the desired postcondition. In this paper we focus on sampled data
systems--systems in which a digital controller interrupts a physical plant in a
periodic manner, a class commonly known in control theory--and furthermore
assume that a controller is given in the form of an imperative program. We
develop a structural approach to input synthesis that features forward and
backward reasoning in program logic for the purpose of reducing a search space.
Although the examples we cover are limited both in size and in structure,
experiments with a prototype implementation suggest potential of our program
logic based approach.Comment: In Proceedings HAS 2014, arXiv:1501.0540
Conformance Testing as Falsification for Cyber-Physical Systems
In Model-Based Design of Cyber-Physical Systems (CPS), it is often desirable
to develop several models of varying fidelity. Models of different fidelity
levels can enable mathematical analysis of the model, control synthesis, faster
simulation etc. Furthermore, when (automatically or manually) transitioning
from a model to its implementation on an actual computational platform, then
again two different versions of the same system are being developed. In all
previous cases, it is necessary to define a rigorous notion of conformance
between different models and between models and their implementations. This
paper argues that conformance should be a measure of distance between systems.
Albeit a range of theoretical distance notions exists, a way to compute such
distances for industrial size systems and models has not been proposed yet.
This paper addresses exactly this problem. A universal notion of conformance as
closeness between systems is rigorously defined, and evidence is presented that
this implies a number of other application-dependent conformance notions. An
algorithm for detecting that two systems are not conformant is then proposed,
which uses existing proven tools. A method is also proposed to measure the
degree of conformance between two systems. The results are demonstrated on a
range of models
Dependability Analysis of Control Systems using SystemC and Statistical Model Checking
Stochastic Petri nets are commonly used for modeling distributed systems in
order to study their performance and dependability. This paper proposes a
realization of stochastic Petri nets in SystemC for modeling large embedded
control systems. Then statistical model checking is used to analyze the
dependability of the constructed model. Our verification framework allows users
to express a wide range of useful properties to be verified which is
illustrated through a case study
Deriving real-time action systems with multiple time bands using algebraic reasoning
The verify-while-develop paradigm allows one to incrementally develop programs from their specifications using a series of calculations against the remaining proof obligations. This paper presents a derivation method for real-time systems with realistic constraints on their behaviour. We develop a high-level interval-based logic that provides flexibility in an implementation, yet allows algebraic reasoning over multiple granularities and sampling multiple sensors with delay. The semantics of an action system is given in terms of interval predicates and algebraic operators to unify the logics for an action system and its properties, which in turn simplifies the calculations and derivations
Time-Staging Enhancement of Hybrid System Falsification
Optimization-based falsification employs stochastic optimization algorithms
to search for error input of hybrid systems. In this paper we introduce a
simple idea to enhance falsification, namely time staging, that allows the
time-causal structure of time-dependent signals to be exploited by the
optimizers. Time staging consists of running a falsification solver multiple
times, from one interval to another, incrementally constructing an input signal
candidate. Our experiments show that time staging can dramatically increase
performance in some realistic examples. We also present theoretical results
that suggest the kinds of models and specifications for which time staging is
likely to be effective
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