Statistical Model Checking with Change Detection

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, eg, random data and unreliable components. It is thus 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. 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 to conduct 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 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 Bounded Linear Temporal Logic. 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, the mechanism for automatically instrumenting the model‐under‐verification, and the interaction with statistical model checking algorithms.

Rare event simulation for highly dependable systems with fast repairs

A parameter generation method based on model checking is proposed to tackle the parameter selection of hierarchical scheduling systems in Integrated Modular Avionics (IMA) by combining the classical symbolic model checking and the Statistical Model Checking (SMC). It builds a generic timed automata network to describe the temporal behavior of hierarchical systems. A distributed genetic algorithm is adopted to search the optimum partition parameters with respect to processor utilization while guaranteeing the schedulability of the system, which is formulated as safety properties of symbolic model checking and hypothesis testing of SMC. Comparing with the widely-used response time analysis, the formal model of this method is more expressive to cover complex features. The application of SMC alleviates the "state space explosion" of classical model checking. Finally, the parameter generation experiments show that the present method is able to find the global optimum solutions in the parameter space.

Statistical model checking uses simulation to overcome the state space explosion problem in formal verification. Yet its runtime explodes when faced with rare events, unless a rare event simulation method like importance splitting is used. The effectiveness of importance splitting hinges on nontrivial model-specific inputs: an importance function with matching splitting thresholds. This prevents its use by non-experts for general classes of models. In this paper, we propose new method combinations with the goal of fully automating the selection of all parameters for importance splitting. We focus on transient (reachability) properties, which particularly challenged previous techniques, and present an exhaustive practical evaluation of the new approaches on case studies from the literature. We find that using Restart simulations with a compositionally constructed importance function and thresholds determined via a new expected success method most reliably succeeds and performs very well. Our implementation within the Modest Toolset supports various classes of formal stochastic models and is publicly available.

In the railway domain, an interlocking is the system ensuring safe train traffic inside a station by controlling its active elements such as the signals or points. Modern interlockings are configured using particular data, called application data, reflecting the track layout and defining the actions that the interlocking can take. The safety of the train traffic relies thereby on application data correctness, errors inside them can cause safety issues such as derailments or collisions. Given the high level of safety required by such a system, its verification is a critical concern. In addition to the safety, an interlocking must also ensure that availability properties, stating that no train would be stopped forever in a station, are satisfied. Most of the research dealing with this verification relies on model checking. However, due to the state space explosion problem, this approach does not scale for large stations. More recently, a discrete event simulation approach limiting the verification to a set of likely scenarios, was proposed. The simulation enables the verification of larger stations, but with no proof that all the interesting scenarios are covered by the simulation. In this paper, we apply an intermediate statistical model checking approach, offering both the advantages of model checking and simulation. Even if exhaustiveness is not obtained, statistical model checking evaluates with a parametrizable confidence the reliability and the availability of the entire system.

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

Statistical model checking uses Monte Carlo simulation to analyse stochastic formal models. It avoids state space explosion, but requires rare event simulation techniques to efficiently estimate very low probabilities. One such technique is RESTART. Villen-Altamirano recently showed—by way of a theoretical study and ad-hoc implementation—that a generalisation of RESTART to prolonged retrials offers improved performance. In this paper, we demonstrate our independent replication of the original experimental results. We implemented RESTART with prolonged retrials in the and modes tools, and apply them to the models used originally. To do so, we had to resolve ambiguities in the original work, and refine our setup multiple times. We ultimately confirm the previous results, but our experience also highlights the need for precise documentation of experiments to enable replicability in computer science.

Statistical model checking uses Monte Carlo simulation to analyse stochastic formal models. It avoids state space explosion, but requires rare event simulation techniques to efficiently estimate very low probabilities. One such technique is textsc {Restart}. Villén-Altamirano recently showed—by way of a theoretical study and ad-hoc implementation—that a generalisation of textsc {Restart} to prolonged retrials offers improved performance. In this paper, we demonstrate our independent replication of the original experimental results. We implemented textsc {Restart} with prolonged retrials in the and modes tools, and apply them to the models used originally. To do so, we had to resolve ambiguities in the original work, and refine our setup multiple times. We ultimately confirm the previous results, but our experience also highlights the need for precise documentation of experiments to enable replicability in computer science.

Robots are soon going to be deployed in non-industrial environments. Before society can take such a step, it is necessary to endow complex robotic systems with mechanisms that make them reliable enough to operate in situations where the human factor is predominant. This calls for the development of robotic frameworks that can soundly guarantee that a collection of properties are verified at all times during operation. While developing a mission plan, robots should take into account factors such as human physiology. In this paper, we present an example of how a robotic application that involves human interaction can be modeled through hybrid automata, and analyzed by using statistical model-checking. We exploit statistical techniques to determine the probability with which some properties are verified, thus easing the state-space explosion problem. The analysis is performed using the Uppaal tool. In addition, we used Uppaal to run simulations that allowed us to show non-trivial time dynamics that describe the behavior of the real system, including human-related variables. Overall, this process allows developers to gain useful insights into their application and to make decisions about how to improve it to balance efficiency and user satisfaction.

Comprehensive understanding of biology can only be achieved by integrating several type of data and models across wide time and space scales, ranging from molecules to ecology. While substantial advances have been obtained through the reductionist approach, where specific sybsystems are investigated separately, usually at microscopic scale, the integration of structural and dynamical concepts along several scales holds the key for major breakthroughs. Such endeavours are part of the so-called Systems Biology area. This article discusses and illustrates how complex networks, image analysis, signal processing and artificial intelligence can be integrated into systems biology research, providing an unprecedented opportunity for scientific and technologic advances. The generality of complex networks for system representation allows this type of structures to be effectively used to model both the architecture and function of virtually any biological entity. At the same time, image analysis paves the way not only for the characterization of phenotipic traits, but also for mapping several geometric biological structures (e.g. neuronal systems, bone canals, cell organelles, etc.) as complex networks. Several concepts and methods from signal processing can then be applied in order to characterize, classify and model the structure and dynamics in such network representations. Artificial intelligence approaches, especially pattern recognition, are also necessary in order to automate, integrate and interprete all such myriad of data and models. In addition to discussing, in a brief though accessible fashion, each of these areas as well as their integration into systems biology, the current work also illustrates the respective potential of this approach with respect to some of the author's recent works.

International audience

UDL/I is a hardware design language for ASIC design developed in a Japanese LSI design language standardization project. A formal semantics of UDL/I is presented. There is no established way to define semantics of hardware design languages, because there is no general computation model for them. A definition method of formal semantics for UDL/I and semantics suitable for a standard language are developed. The basic idea is that the semantics of the language should be defined independently of simulators. The language provides an abstract model of hardware. The role of the language designer is to provide an abstract model of hardware which is natural and general for hardware designers and CAD engineers. The authors introduce a novel behavior model of hardware and define the semantics with a nondeterministic feature. >

In this paper we study the problem of constructing histograms from high-speed time-changing data streams. Learning in this context requires the ability to process examples once at the rate they arrive, maintaining a histogram consistent with the most recent data, and forgetting out-date data whenever a change in the distribution is detected. To construct histogram from high-speed data streams we use the two layer structure used in the Partition Incremental Discretization (PiD) algorithm. Our contribution is a new method to detect whenever a change in the distribution generating examples occurs. The base idea consists of monitoring distributions from two different time windows: the reference time window, that reflects the distribution observed in the past; and the current time window reflecting the distribution observed in the most recent data. We compare both distributions and signal a change whenever they are greater than a threshold value, using three different methods: the Entropy Absolute Difference, the Kullback-Leibler divergence and the Cosine Distance. The experimental results suggest that Kullback-Leibler divergence exhibit high probability in change detection, faster detection rates, with few false positives alarms.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Advances in numerical algorithms, improvement of computational power and progress in remote sensing have led to the development of global flood models (GFMs), which promise to be a useful tool for large-scale flood risk management. However, performance and reliability of GFMs, especially in data-scarce regions, is still uncertain, as they are difficult to validate. Here we aim at contributing to develop alternative, more flexible, and consistent methods for GFM validation by applying a change detection analysis on synthetic aperture radar (CD-SAR) imagery obtained from the Sentinel-1 imagery, on a cloud-based geospatial analysis platform. The study addresses two main objectives. First, to validate four widely adopted GFMs with flood maps generated through the proposed CD-SAR approach. This exercise was conducted for eight different large river basins on four continents, to account for a diverse range of hydro-climatic environments. Second, to compare CD-SAR-derived flood maps with those obtained from alternative remote sensing sources. These comparative results offer valuable insights into the reliability of CD-SAR data as a validation tool, more specifically how it stacks up against flood maps generated by other remote sensing techniques.