Counterexample-guided abstraction refinement (CEGAR) has been en vogue for the automatic verification of very large systems in the past years. When trying to apply CEGAR to the verification of probabilistic systems, various foundational questions arise. This paper explores them in the context of predicate abstraction.

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Abstract. In this paper, we propose a new randomised algorithm for deciding language equivalence for probabilistic automata. This algorithm is based on polynomial identity testing and thus returns an answer with an error probability that can be made arbitrarily small. We implemented our algorithm, as well as deterministic algorithms of Tzeng and Doyen et al., optimised for running time whilst adequately handling issues of numerical stability. We conducted extensive benchmarking experiments, including the verification of randomised anonymity protocols, the outcome of which establishes that the randomised algorithm significantly outperforms the deterministic ones in a majority of our test cases. Finally, we also provide fine-grained analytical bounds on the complexity of these algorithms, accounting for the differences in performance.

Abstract. This paper investigates relative precision and optimality of analyses for concurrent probabilistic systems. Aiming at the problem at the heart of probabilistic model checking -computing the probability of reaching a particular set of states -we leverage the theory of abstract interpretation. With a focus on predicate abstraction, we develop the first abstract-interpretation framework for Markov decision processes which admits to compute both lower and upper bounds on reachability probabilities. Further, we describe how to compute and approximate such abstractions using abstraction refinement and give experimental results.

We present PASS, a tool that analyzes concurrent probabilistic programs, which map to potentially infinite Markov decision processes. PASS is based on predicate abstraction and abstraction refinement and scales to programs far beyond the reach of numerical methods which operate on the full state space of the model. The computational engines we use are SMT solvers to compute finite abstractions, numerical methods to compute probabilities and interpolation as part of abstraction refinement. PASS has been successfully applied to network protocols and serves as a test platform for different refinement methods.

Abstract. The design of complex concurrent systems often involves intricate performance and dependability considerations. Continuous-time Markov chains (CTMCs) are a widely used modeling formalism, where performance and dependability properties are analyzable by model checking. We present INFAMY, a model checker for arbitrarily structured infinite-state CTMCs. It checks probabilistic timing properties expressible in continuous stochastic logic (CSL). Conventional model checkers explore the given model exhaustively, which is often costly, due to state explosion, and impossible if the model is infinite. INFAMY only explores the model up to a finite depth, with the depth bound being computed on-the-fly. The computation of depth bounds is configurable to adapt to the characteristics of different classes of models. Introducing INFAMYContinuous-time Markov chains (CTMCs) are widely used in performance and dependability analysis and biological modeling. Properties are typically specified in continuous stochastic logic (CSL) [1], a logic inspired by CTL. In CSL, the until operator is equipped with a time interval to express properties such as: "The probability to reach a goal within 2 hours while maintaining a probability of at least 0.5 of communicating periodically (every five minutes) with a base station, is at least 0.9" via P ≥0.9 P ≥0.5 3 ≤5 communicate U ≤120 goal . CSL model checking amounts to analysis of the transient (time-dependent) probability vectors [1], typically carried out by uniformization, where the transient probability is expressed by a weighted infinite sum (weights are given by a Poisson process). The standard methodology in CSL model checking is to truncate the infinite sum up to some pre-specified accuracy [2]. Outside the model checking arena, ideas have been developed [3,4,5] which not only truncate the infinite sum, but also the matrix representing the system, which admits transient analysis of CTMCs with large or even infinite state spaces, provided they are given implicitly in a