Bounded saturation-based CTL model checking

Formal verification is becoming a fundamental step of safety-critical and model-based software development. As part of the verification process, model checking is one of the current advanced techniques to analyze the behavior of a system. Symbolic model checking is an efficient approach to handling even complex models with huge state spaces. Saturation is a symbolic algorithm with a special iteration strategy, which is efficient for asynchronous models. Recent advances have resulted in many new kinds of saturation-based algorithms for state space generation and bounded state space generation and also for structural model checking. In this paper, we examine how the combination of two advanced model checking algorithms – bounded saturation and saturation-based structural model checking – can be used to verify systems. Our work is the first attempt to combine these approaches, and this way we are able to handle and examine complex or even infinite state systems. Our measurements show that we can exploit the efficiency of saturation in bounded model checking

Model checking Markov chains : techniques and tools

This dissertation deals with four important aspects of model checking Markov chains: the development of efficient model-checking tools, the improvement of model-checking algorithms, the efficiency of the state-space reduction techniques, and the development of simulation-based model-checking procedures. First, we introduce MRMC, a model checker for DMRMs and CMRMs, that supports reward extensions of PCTL and CSL. We study the efficiency, of the tool in comparison with probabilistic model checkers such as E -MC2, PRISM, Ymer and VESTA, and focus on fully probabilistic systems. Further, we provide a precise procedure for steady-state detection for time-bounded reachabiity on CTMCs. After what we study the effect of bisimulation minimization in model checking of monolithic DTMCs, CTMCs and the variants thereof with rewards. We conclude our work by deriving techniques based on discrete-event sijulation and sequential confidence intervals for model checking CSL properties on CTMCs.\u

On-the-fly Probabilistic Model Checking

Model checking approaches can be divided into two broad categories: global approaches that determine the set of all states in a model M that satisfy a temporal logic formula f, and local approaches in which, given a state s in M, the procedure determines whether s satisfies f. When s is a term of a process language, the model checking procedure can be executed "on-the-fly", driven by the syntactical structure of s. For certain classes of systems, e.g. those composed of many parallel components, the local approach is preferable because, depending on the specific property, it may be sufficient to generate and inspect only a relatively small part of the state space. We propose an efficient, on-the-fly, PCTL model checking procedure that is parametric with respect to the semantic interpretation of the language. The procedure comprises both bounded and unbounded until modalities. The correctness of the procedure is shown and its efficiency is compared with a global PCTL model checker on representative applications.Comment: In Proceedings ICE 2014, arXiv:1410.701

Efficient Parallel Path Checking for Linear-Time Temporal Logic With Past and Bounds

Path checking, the special case of the model checking problem where the model under consideration is a single path, plays an important role in monitoring, testing, and verification. We prove that for linear-time temporal logic (LTL), path checking can be efficiently parallelized. In addition to the core logic, we consider the extensions of LTL with bounded-future (BLTL) and past-time (LTL+Past) operators. Even though both extensions improve the succinctness of the logic exponentially, path checking remains efficiently parallelizable: Our algorithm for LTL, LTL+Past, and BLTL+Past is in AC^1(logDCFL) \subseteq NC

Efficient Parallel Statistical Model Checking of Biochemical Networks

We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture