Statistical probabilistic model checking with a focus on time-bounded properties

Younes, Håkan L. S.; Simmons, Reid

doi:10.1016/j.ic.2006.05.002

Cited by 174 publications

(166 citation statements)

References 27 publications

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“…Younes and Simmons [15] extend Sen et al's approach in order to verify probabilistic and time-based properties of black-box systems, as generalised by semiMarkov decision processes. The authors adopt the sequential probability ratio test (SPRT) and verify system traces as stochastic discrete event based models.…”

Section: Statistical Verificationmentioning

confidence: 99%

“…Thus it can be represented as a Bernoulli process, where each random variable X is a Bernoulli trial, mapping Ω → {true, false}, such that X = true with probability p and X = false with probability 1 − p (p ∈ P). The probability distribution of a sum of n Bernoulli trials with parameter p can be represented as a binomial distribution with parameter n and p as follows: In this a way, we model system states of a femtocell as a stochastic discrete event system (denoted as M), which is similar to the work introduced in [15]. The state space of such a system can be represented as sequential observations at times t 1 , t 2 , · · · , for example, a sequence of overlap events.…”

Section: Modelling Overlap Load and Gap Violations As Stochastic Dismentioning

confidence: 99%

“…In contrast to observing the average value of a desired property, statistical verification [11,12,15] is a technique that checks the probabilistic correctness and satisfaction of a system against its desired behaviour. It has two major benefits: first, it adopts a formal mathematical specification to describe the desired system behaviour without ambiguity; second, it measures a property more accurately than the average value because it considers the probabilistic likelihood that a property is violated.…”

Section: Introductionmentioning

confidence: 99%

“…It has two major benefits: first, it adopts a formal mathematical specification to describe the desired system behaviour without ambiguity; second, it measures a property more accurately than the average value because it considers the probabilistic likelihood that a property is violated. The possibility of false positives and negatives can be bounded below a required level [15]. When property violations occur frequently, a fast response to trigger re-optimisation is needed rather than waiting for a given timeout (i.e., false negatives).…”

Section: Introductionmentioning

confidence: 99%

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Femtocell Coverage Optimisation Using Statistical Verification

Pietzuch

2011

Networking 2011

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Abstract. Femtocells are small base stations that provide radio coverage for mobile devices in homes or office areas. In this paper, we consider the optimisation of a number of femtocells that provide joint coverage in enterprise environments. In such an environment, femtocells should minimise coverage overlap and coverage holes and ensure a balanced traffic workload among them. We use statistical verification techniques to monitor the probabilistic correctness of a given femtocell configuration at runtime. If there is any violation of the desired level of service, a self-optimisation procedure is triggered to improve the current configuration. Our evaluation results show that, compared with fixed time, interval-based optimisation, our approach achieves better coverage and can detect goal violations quickly with a given level of confidence when they occur frequently. It can also avoid unnecessary self-optimisation cycles, reducing the cost of self-optimisation.

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Section: Statistical Verificationmentioning

confidence: 99%

Section: Modelling Overlap Load and Gap Violations As Stochastic Dismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Femtocell Coverage Optimisation Using Statistical Verification

Pietzuch

2011

Networking 2011

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“…Unfortunately, both the complexity and the heterogeneous nature of the constituent systems prevent this solution. To solve this problem, we rely on Statistical Model Checking (SMC) [4]. SMC works by monitoring executions of the system, and rely on statistics to assess the overall correctness.…”

Section: Introductionmentioning

confidence: 99%

An Application of SMC to continuous validation of heterogeneous systems

Arnold¹,

Baleani²,

Ferrari³

et al. 2017

EAI Endorsed Transactions on Industrial Networks and Intelligen

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This paper considers the rigorous design of Systems of Systems (SoS), i.e. systems composed of a set of heterogeneous components whose number evolves with time. Such components cooperate to accomplish functions that they could not achieve in isolation. Examples of SoS include smart cities or airport management system. The dynamical evolution of SoS behavior and architecture makes it impossible to design an appropriate solution beforehand. Consequently, existing approaches build on an iterative process that takes SoS evolution into account. A key challenge in this process is the ability to reason about and analyze a given view of the SoS (on a fixed number of SoS constituents) with respect to a set of goals, and use the results to eventually predict the evolution of the SoS. To address this challenge, we rely on a scalable formal verification technique known as Statistical Model Checking (SMC). SMC quantifies how close the current view is from achieving a given mission. We integrate SMC with existing industrial practice, by addressing both methodological and technological issues. Our contribution is: (1) a methodology for validation of SoS formal requirements; (2) a formal specification language able to express complex SoS requirements; (3) the adoption of current industry standards for simulation and heterogeneous systems integration ; (4) a robust SMC tool-chain integrated with system design tools used in practice. We illustrate the application of our SMC tool-chain and the obtained results on a case study.

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Formal verification of probabilistic SystemC models with statistical model checking

Ngo

Legay

2017

J Software Evolu Process

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SUMMARYTransaction-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.

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Statistical probabilistic model checking with a focus on time-bounded properties

Cited by 174 publications

References 27 publications

Femtocell Coverage Optimisation Using Statistical Verification

Femtocell Coverage Optimisation Using Statistical Verification

An Application of SMC to continuous validation of heterogeneous systems

Formal verification of probabilistic SystemC models with statistical model checking

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