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We investigate the problem of model checking Interval-valued Discrete-time Markov Chains (IDTMC). IDTMCs are discrete-time finite Markov Chains for which the exact transition probabilities are not known. Instead in IDTMCs, each transition is associated with an interval in which the actual transition probability must lie. We consider two semantic interpretations for the uncertainty in the transition probabilities of an IDTMC. In the first interpretation, we think of an IDTMC as representing a (possibly uncountable) family of (classical) discrete-time Markov Chains, where each member of the family is a Markov Chain whose transition probabilities lie within the interval range given in the IDTMC. This semantic interpretation we call Uncertain Markov Chains (UMC). In the second semantics for an IDTMC, which we call Interval Markov Decision Process (IMDP), we view the uncertainty as being resolved through non-determinism. In other words, each time a state is visited, we adversarially pick a transition distribution that respects the interval constraints, and take a probabilistic step according to the chosen distribution. We show that the PCTL model checking problem for both Uncertain Markov Chain semantics and Interval Markov Decision Process semantics is decidable in PSPACE. We also prove lower bounds for these model checking problems. [ABSTRACT FROM AUTHOR]

Statistical methods to model check stochastic systems have been, thus far, developed only for a sublogic of continuous stochastic logic (CSL) that does not have steady state operator and unbounded until formulas. In this paper, we present a statistical model checking algorithm that also verifies CSL formulas with unbounded untils. The algorithm is based on Monte Carlo simulation of the model and hypothesis testing of the samples, as opposed to sequential hypothesis testing. We have implemented the algorithm in a tool called VESTA, and found it to be effective in verifying several examples. [ABSTRACT FROM AUTHOR]

We study congruences on words in order to characterize the class of visibly pushdown languages (Vpl), a subclass of context-free languages. For any language L, we define a natural congruence on words that resembles the syntactic congruence for regular languages, such that this congruence is of finite index if, and only if, L is a Vpl. We then study the problem of finding canonical minimal deterministic automata for Vpls. Though Vpls in general do not have unique minimal automata, we consider a subclass of VPAs called k-module single-entry VPAs that correspond to programs with recursive procedures without input parameters, and show that the class of well-matched Vpls do indeed have unique minimal k-module single-entry automata. We also give a polynomial time algorithm that minimizes such k-module single-entry VPAs. [ABSTRACT FROM AUTHOR]

Traditional network simulators perform well in evaluating the performance of network protocols but lack the capability of verifying the correctness of protocols. To address this problem, we have extended the J-Sim network simulator with a model checking capability that explores the state space of a network protocol to find an execution that violates a safety invariant. In this paper, we demonstrate the usefulness of this integrated tool for verification and performance evaluation by analyzing two widely used and important network protocols: AODV and directed diffusion. Our analysis discovered a previously unknown bug in the J-Sim implementation of AODV. More importantly, we also discovered a serious deficiency in directed diffusion. To enable the analysis of these fairly complex protocols, we needed to develop protocol-specific search heuristics that guide state-space exploration. We report our findings on discovering good search heuristics to analyze network protocols similar to AODV and directed diffusion. [ABSTRACT FROM AUTHOR]

When a system fails to satisfy its specification, the model checker produces an error trace (or counter-example) that demonstrates an undesirable behavior, which is then used in debugging the system. Error explanation is the task of discovering errors in the system or the reasons why the system exhibits the error trace. While there has been considerable recent interest in automating this task and developing tools based on different heuristics, there has been very little effort in characterizing the computational complexity of the problem of error explanation. In this paper, we study the complexity of two popular heuristics used in error explanation. The first approach tries to compute the smallest number of system changes that need to be made in order to ensure that the given counter-example is no longer exhibited, with the intuition being that these changes are the errors that need fixing. The second approach relies on the observation that differences between correct and faulty runs of a system shed considerable light on the sources of errors. In this approach, one tries to compute the correct trace of the system that is closest to the counter-example. We consider three commonly used abstractions to model programs and systems, namely, finite state Mealy machines, extended finite state machines and pushdown automata. We show that the first approach of trying to find the fewest program changes is NP-complete no matter which of the three formal models is used to represent the system. Moreover we show that no polynomial factor approximation algorithm for computing the smallest set of changes is possible, unless P = NP. For the second approach, we present a polynomial time algorithm that finds the closest correct trace, when the program is represented by a Mealy machine or a pushdown automata. When the program is represented by an extended finite state machine, the problem is once again NP-complete, and no polynomial factor approximation algorithm is likely. [ABSTRACT FROM AUTHOR]

A novel machine learning based approach was proposed recently as a complementary technique to the acceleration based methods for verifying infinite state systems. In this method, the set of states satisfying a fixpoint property is learnt as opposed to being iteratively computed. We extend the machine learning based approach to verifying general ω-regular properties that include both safety and liveness. To achieve this, we first develop a new fixpoint based characterization for the verification of ω-regular properties. Using this characterization, we present a general framework for verifying infinite state systems. We then instantiate our approach to the context of regular model checking where states are represented as strings over a finite alphabet and the transition relation of the system is given as a finite state transducer; unlike previous learning based algorithms, we make no assumption about the transducer being length-preserving. Using Angluin's L* algorithm for learning regular languages, we develop an algorithm for verification of ω-regular properties of such infinite state systems. The algorithm is a complete verification procedure for systems for whom the fixpoint can be represented as a regular set. We have implemented the technique in a tool called Lever and use it to analyze some examples. [ABSTRACT FROM AUTHOR]

As the complexity of systems grows, the correctness of systems becomes harder to achieve. This difficulty promotes a run-time monitoring technique as a promising complementary methodology for higher system assurance. To formalize and understand the computational nature of run-time monitoring is a key to utilize this valuable technique. In this paper, we formalize the notion of run-time monitoring of reactive systems in terms of ω-languages and show that the language of Monitoring and Checking (MaC) architecture, called MEDL, is expressive enough for the run-time monitoring. First, we provide a descriptive theory for the class of monitorable languages and show that this class of languages coincides with the class Π01 of the Arithmetic hierarchy. Second, we introduce a class of automata with storage that can be used to describe the class of monitorable languages using connections to the Arithmetic hierarchy. Finally, we show that MEDL can express the class of monitorable languages via the correspondence between MEDL and the automata with storage. [ABSTRACT FROM AUTHOR]

In this paper we present lower bounds and rewriting algorithms for testing membership of a word in a regular language described by an extended regular expression. Motivated by intuitions from monitoring and testing, where the words to be tested (execution traces) are typically much longer than the size of the regular expressions (patterns or requirements), and by the fact that in many applications the traces are only available incrementally, on an event by event basis, our algorithms are based on an event-consumption idea: a just arrived event is "consumed" by the regular expression, i.e., the regular expression modifies itself into another expression discarding the event. We present an exponential space lower bound for monitoring extended regular expressions and argue that the presented rewriting-based algorithms, besides their simplicity and elegance, are practical and almost as good as one can hope. We experimented with and evaluated our algorithms in Maude. [ABSTRACT FROM AUTHOR]