Automata-theoretic Model Checking Research Articles

Learning-based black box checking for [formula omitted]-safety hyperproperties

The L∗ algorithm proposed by Angluin is a standard approach to automata learning of unknown systems that generate regular languages. Black box checking (BBC) based on the L∗ algorithm is a promising formal verification of unknown systems. On the other hand, a k-safety hyperproperty is a hyperproperty that proscribes “bad things”. For example, it can express safety policies for safety-critical and safety-related systems. However, conventional BBC methods based on the L∗ algorithm focuses only on properties, not on hyperproperties. Therefore, this paper proposes an algorithm that checks whether a black box system satisfies a regular k-safety hyperproperty based on the Angluin’s L∗ algorithm. In the proposed algorithm, k-bad prefix automata that violate the regular k-safety hyperproperty are generated from the regular k-safety hyperproperty described as a HyperLTL formula. Because the generated k-bad prefix automata are finite automata, the verification of the k-safety hyperproperty for black box systems can be conducted by automata-theoretic model checking. It is demonstrated that the proposed algorithm can verify noninterference, described as a 2-safety hyperproperty, with an illustrative example.

Model measuring for discrete and hybrid systems

We define the model-measuring problem: given a model M and specification φ, what is the maximal distance ρ such that all models M′ within distance ρ from M satisfy (or violate) φ. The model-measuring problem presupposes a distance function on models. We concentrate on automatic distance functions, which are defined by weighted automata. The model-measuring problem subsumes several generalizations of the classical model-checking problem, in particular, quantitative model-checking problems that measure the degree of satisfaction of a specification; robustness problems that measure how much a model can be perturbed without violating the specification; and parameter synthesis for hybrid systems. We show that for automatic distance functions, and (a) ω-regular linear-time, (b) ω-regular branching-time, and (c) hybrid specifications, the model-measuring problem can be solved.We use automata-theoretic model-checking methods for model measuring, replacing the emptiness question for word, tree, and hybrid automata by the optimal-value question for the weighted versions of these automata. For automata over words and trees, we consider weighted automata that accumulate weights by maximizing, summing, discounting, and limit averaging. For hybrid automata, we consider monotonic (parametric) hybrid automata, a hybrid counterpart of (discrete) weighted automata.We give several examples of using the model-measuring problem to compute various notions of robustness and quantitative satisfaction for temporal specifications. Further, we propose the modeling framework for model measuring to ease the specification and reduce the likelihood of errors in modeling.Finally, we present a variant of the model-measuring problem, called the model-repair problem. The model-repair problem applies to models that do not satisfy the specification; it can be used to derive restrictions, under which the model satisfies the specification, i.e., to repair the model.

Fairness in automata theoretic model checking

Fairness in automata theoretic model checking

Effective State Exploration for Model Checking on a Shared Memory Architecture

In this paper we present results from experimental studies investigating implementation strategies for explicit-state temporal-logic model checking on a virtual shared-memory high-performance parallel machine architecture. In particular, a parallel state exploration algorithm using a two-queue structure for load balancing is proposed and its performance analysed at the hand of experimental studies. We then discuss implementation issues for parallel automata-theoretic model checking using this parallel state exploration algorithm.