TACAS'2011, 17th International ConferenceTools and Algorithms for the Construction and Analysis of Systems.

We propose a model independent procedure for verifying properties of discrete event systems. The dynamics of such systems can be very complex, making them hard to analyze, so we resort to methods based on Monte Carlo simulation and statistical hypothesis testing. The verification is probabilistic in two senses. First, the properties, expressed as CSL formulas, can be probabilistic. Second, the result of the verification is probabilistic, and the probability of error is bounded by two parameters passed to the verification procedure. The verification of properties can be carried out in an anytime manner by starting off with loose error bounds, and gradually tightening these bounds.

Probabilistic verification of discrete event systems using acceptance sampling

Test generation from P systems using model checking

Spiking neural P (SN P) systems are a class of discrete neuron-inspired computation models, where information is encoded by the numbers of spikes in neurons and the timing of spikes. However, due to the discontinuous nature of the integrate-and-fire behavior of neurons and the symbolic representation of information, SN P systems are incompatible with the gradient descent-based training algorithms, such as the backpropagation algorithm, and lack the capability of processing the numerical representation of information. In this work, motivated by the numerical nature of numerical P (NP) systems in the area of membrane computing, a novel class of SN P systems is proposed, called numerical SN P (NSN P) systems. More precisely, information is encoded by the values of variables, and the integrate-and-fire way of neurons and the distribution of produced values are described by continuous production functions. The computation power of NSN P systems is investigated. We prove that NSN P is Turing universal as number generating devices, where the production functions in each neuron are linear functions, each involving at most one variable; as number accepting devices, NSN P systems are proved to be universal as well, even if each neuron contains only one production function. These results show that even if a single neuron is simple in the sense that it contains one or two production functions and the production functions in each neuron are linear functions with one variable, a network of simple neurons are still computationally powerful. With the powerful computation power and the characteristic of continuous production functions, developing learning algorithms for NSN P systems is potentially exploitable.

Numerical Spiking Neural P Systems

The issue of reversibility in computational paradigms has gained interest in recent years In this paper we investigate how to reverse steps in membrane systems computations The problem is that computation steps in membrane systems do not preserve all the information that has to be used when reversing them We try to formalize the relevant information needed, and we show that the proposed approach enjoy the so called loop lemma, which basically assures that the undoing obtained by reversely applying rules is correct

Reversing Steps in Membrane Systems Computations

P systems are the computational models introduced in the context of membrane computing, a computational paradigm within the more general area of unconventional computing. Kernel P (kP) systems are defined to unify the specification of different variants of P systems, motivated by challenging theoretical aspects and the need to model different problems. kP systems are supported by a software framework, called kPWorkbench, which integrates a set of related simulation and verification methodologies and tools. In this paper, we present an extension to kPWorkbench with a new model checking framework supporting the formal verification of kP system models. This framework supports both LTL and CTL properties. To make the property specification an easier task, we propose a property language, composed of natural language statements. We demonstrate our proposed methodology with an example.

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An Integrated Model Checking Toolset for Kernel P Systems

Kernel P systems: From modelling to verification and testing

Motivation: Formal verification is a computational approach that checks system correctness (in relation to a desired functionality). It has been widely used in engineering applications to verify that systems work correctly. Model checking, an algorithmic approach to verification, looks at whether a system model satisfies its requirements specification. This approach has been applied to a large number of models in systems and synthetic biology as well as in systems medicine. Model checking is, however, computationally very expensive, and is not scalable to large models and systems. Consequently, statistical model checking (SMC), which relaxes some of the constraints of model checking, has been introduced to address this drawback. Several SMC tools have been developed; however, the performance of each tool significantly varies according to the system model in question and the type of requirements being verified. This makes it hard to know, a priori, which one to use for a given model and requirement, as choosing the most efficient tool for any biological application requires a significant degree of computational expertise, not usually available in biology labs. The objective of this paper is to introduce a method and provide a tool leading to the automatic selection of the most appropriate model checker for the system of interest. Results: We provide a system that can automatically predict the fastest model checking tool for a given biological model. Our results show that one can make predictions of high confidence, with over 90% accuracy. This implies significant performance gain in verification time and substantially reduces the "usability barrier" enabling biologists to have access to this powerful computational technology. Availability and implementation: Statistical Model Checker (SMC) Predictor tool is available at http://www.smcpredictor.com. Contact: mebakir1@sheffield.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

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Automatic selection of verification tools for efficient analysis of biochemical models.

Although testing is an essential part of software development, until re- cently, P system testing has been completely neglected. Mutation testing (mutation analysis) is a structural software testing method which involves modifying the pro- gram in small ways. In this paper, we provide a formal way of generating mutants for systems specified by context-free grammars. Furthermore, the paper shows how the proposed method can be used to construct mutants for a P system specification.

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Mutation Based Testing of P Systems

Formal modelling is essential for precisely defining, understanding and reasoning when designing complex systems, such as cyberphysical systems. In this paper we present a formal specification using Event-B and Rodin platform for a case study of a cruise control system for a hybrid propulsion vehicle and electric bicycle (e-Bike). Our work uses the Event- B method, a formal approach for reliable systems specification and verification, being supported by the Rodin platform, based on theorem proving, allowing a stepwise specification process based on refinement. We also use, from the same platform, the ProB model checker for the verification of the B-Machine and iUML plug-in to visualize our model. This approach shows the benefits of using a formal modelling platform, in the context of cyberphysical systems, which provides multiple ways of analysing a system.

Marian Gheorghe

Papers

An Integrated Model Checking Toolset for Kernel P Systems

Kernel P systems: From modelling to verification and testing

Automatic selection of verification tools for efficient analysis of biochemical models.

Mutation Based Testing of P Systems

Formal Modelling of Cruise Control System Using Event-B and Rodin Platform