3,407 research outputs found
Assume-guarantee verification for probabilistic systems
We present a compositional verification technique for systems that exhibit both probabilistic and nondeterministic behaviour. We adopt an assume- guarantee approach to verification, where both the assumptions made about system components and the guarantees that they provide are regular safety properties, represented by finite automata. Unlike previous proposals for assume-guarantee reasoning about probabilistic systems, our approach does not require that components interact in a fully synchronous fashion. In addition, the compositional verification method is efficient and fully automated, based on a reduction to the problem of multi-objective probabilistic model checking. We present asymmetric and circular assume-guarantee rules, and show how they can be adapted to form quantitative queries, yielding lower and upper bounds on the actual probabilities that a property is satisfied. Our techniques have been implemented and applied to several large case studies, including instances where conventional probabilistic verification is infeasible
Quantitative multi-objective verification for probabilistic systems
We present a verification framework for analysing multiple quantitative objectives of systems that exhibit both nondeterministic and stochastic behaviour. These systems are modelled as probabilistic automata, enriched with cost or reward structures that capture, for example, energy usage or performance metrics. Quantitative properties of these models are expressed in a specification language that incorporates probabilistic safety and liveness properties, expected total cost or reward, and supports multiple objectives of these types. We propose and implement an efficient verification framework for such properties and then present two distinct applications of it: firstly, controller synthesis subject to multiple quantitative objectives; and, secondly, quantitative compositional verification. The practical applicability of both approaches is illustrated with experimental results from several large case studies
Model-based dependability analysis : state-of-the-art, challenges and future outlook
Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis
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
Multi-Objective Model Checking of Markov Decision Processes
We study and provide efficient algorithms for multi-objective model checking
problems for Markov Decision Processes (MDPs). Given an MDP, M, and given
multiple linear-time (\omega -regular or LTL) properties \varphi\_i, and
probabilities r\_i \epsilon [0,1], i=1,...,k, we ask whether there exists a
strategy \sigma for the controller such that, for all i, the probability that a
trajectory of M controlled by \sigma satisfies \varphi\_i is at least r\_i. We
provide an algorithm that decides whether there exists such a strategy and if
so produces it, and which runs in time polynomial in the size of the MDP. Such
a strategy may require the use of both randomization and memory. We also
consider more general multi-objective \omega -regular queries, which we
motivate with an application to assume-guarantee compositional reasoning for
probabilistic systems.
Note that there can be trade-offs between different properties: satisfying
property \varphi\_1 with high probability may necessitate satisfying \varphi\_2
with low probability. Viewing this as a multi-objective optimization problem,
we want information about the "trade-off curve" or Pareto curve for maximizing
the probabilities of different properties. We show that one can compute an
approximate Pareto curve with respect to a set of \omega -regular properties in
time polynomial in the size of the MDP.
Our quantitative upper bounds use LP methods. We also study qualitative
multi-objective model checking problems, and we show that these can be analysed
by purely graph-theoretic methods, even though the strategies may still require
both randomization and memory.Comment: 21 pages, 2 figure
Model checking medium access control for sensor networks
We describe verification of S-MAC, a medium access control protocol designed for wireless sensor networks, by means of the PRISM model checker. The S-MAC protocol is built on top of the IEEE 802.11 standard for wireless ad hoc networks and, as such, it uses the same randomised backoff procedure as a means to avoid collision. In order to minimise energy consumption, in S-MAC, nodes are periodically put into a sleep state. Synchronisation of the sleeping schedules is necessary for the nodes to be able to communicate. Intuitively, energy saving obtained through a periodic sleep mechanism will be at the expense of performance. In previous work on S-MAC verification, a combination of analytical techniques and simulation has been used to confirm the correctness of this intuition for a simplified (abstract) version of the protocol in which the initial schedules coordination phase is assumed correct. We show how we have used the PRISM model checker to verify the behaviour of S-MAC and compare it to that of IEEE 802.11
A synthesis of logic and bio-inspired techniques in the design of dependable systems
Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules
Multi-objective Robust Strategy Synthesis for Interval Markov Decision Processes
Interval Markov decision processes (IMDPs) generalise classical MDPs by
having interval-valued transition probabilities. They provide a powerful
modelling tool for probabilistic systems with an additional variation or
uncertainty that prevents the knowledge of the exact transition probabilities.
In this paper, we consider the problem of multi-objective robust strategy
synthesis for interval MDPs, where the aim is to find a robust strategy that
guarantees the satisfaction of multiple properties at the same time in face of
the transition probability uncertainty. We first show that this problem is
PSPACE-hard. Then, we provide a value iteration-based decision algorithm to
approximate the Pareto set of achievable points. We finally demonstrate the
practical effectiveness of our proposed approaches by applying them on several
case studies using a prototypical tool.Comment: This article is a full version of a paper accepted to the Conference
on Quantitative Evaluation of SysTems (QEST) 201
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