On the sequential massart algorithm for statistical model checking

Several schemes have been provided in Statistical Model Checking (SMC) for the estimation of property occurrence based on predefined confidence and absolute or relative error. Simulations might be however costly if many samples are required and the usual algorithms implemented in statistical model checkers tend to be conservative. Bayesian and rare event techniques can be used to reduce the sample size but they can not be applied without prerequisite or knowledge about the system under scrutiny. Recently, sequential algorithms based on Monte Carlo estimations and Massart bounds have been proposed to reduce the sample size while providing guarantees on error bounds which has been shown to outperform alternative frequentist approaches [15]. In this work, we discuss some features regarding the distribution and the optimisation of these algorithms.No Full Tex

SBIP 2.0: Statistical Model Checking Stochastic Real-time Systems

International audienceThis paper presents a major new release of SBIP, an extensi-ble statistical model checker for Metric (MTL) and Linear-time Temporal Logic (LTL) properties on respectively Generalized Semi-Markov Processes (GSMP), Continuous-Time (CTMC) and Discrete-Time Markov Chain (DTMC) models. The newly added support for MTL, GSMPs, CTMCs and rare events allows to capture both real-time and stochastic aspects, allowing faithful specification, modeling and analysis of real-life systems. SBIP is redesigned as an IDE providing project management, model edition, compilation, simulation, and statistical analysis

Social networking, social media and complex emergencies: an annotated bibliography

Andrew Skuse and Tait Brimacomb

Sequential schemes for frequentist estimation of properties in statistical model checking

National Research Foundation (NRF) Singapor

Towards concolic testing for hybrid systems

Hybrid systems exhibit both continuous and discrete behavior. Analyzing hybrid systems is known to be hard. Inspired by the idea of concolic testing (of programs), we investigate whether we can combine random sampling and symbolic execution in order to effectively verify hybrid systems. We identify a sufficient condition under which such a combination is more effective than random sampling. Furthermore, we analyze different strategies of combining random sampling and symbolic execution and propose an algorithm which allows us to dynamically switch between them so as to reduce the overall cost. Our method has been implemented as a web-based checker named HYCHECKER. HYCHECKER has been evaluated with benchmark hybrid systems and a water treatment system in order to test its effectiveness.CPCI-S(ISTP)[email protected]; [email protected]

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems

An Effective Heuristic for Adaptive Importance Splitting in Statistical Model Checking

International audienceStatistical model checking avoids the intractable growth of states associated with numerical model checking by estimating the prob-ability of a property from simulations. Rare properties pose a challenge because the relative error of the estimate is unbounded. In [13] we de-scribe how importance splitting may be used with SMC to overcome this problem. The basic idea is to decompose a logical property into nested properties whose probabilities are easier to estimate. To improve perfor-mance it is desirable to decompose the property into many equi-probable levels, but logical decomposition alone may be too coarse. In this article we make use of the notion of a score function to improve the granularity of a logical property. We show that such a score function may take advantage of heuristics, so long as it also rigorously respects certain properties. To demonstrate our importance splitting approach we present an optimal adaptive importance splitting algorithm and an heuristic score function. We give experimental results that demonstrate a significant improvement in performance over alternative approaches

Rare Events for Statistical Model Checking: An Overview

International audienceThis invited paper surveys several simulation-based approaches to compute the probability of rare bugs in complex systems. The paper also describes how those techniques can be implemented in the professional toolset Plasma