Exact and approximate strategies for symmetry reduction in model checking

Symmetry reduction techniques can help to combat the state space explosion problem for model checking, but are restricted by the hard problem of determining equivalence of states during search. Consequently, existing symmetry reduction packages can only exploit full symmetry between system components, as checking the equivalence of states is straightforward in this special case. We present a framework for symmetry reduction with an arbitrary group of structural symmetries. By generalising existing techniques for efficiently exploiting symmetry, and introducing an approximate strategy for use with groups for which fast, exact strategies are not available, our approach allows for significant state-space reduction with minimal time overhead. We show how computational group theoretic techniques can be used to analyse the structure of a symmetry group so that an appropriate symmetry reduction strategy can be chosen, and we describe a symmetry reduction package for the Spin model checker which interfaces with the computational algebra system Gap. Experimental results on a variety of Promela models illustrate the effectiveness of our methods

Exact and approximate strategies for symmetry reduction in model checking.

Abstract. Symmetry reduction techniques can help to combat the state space explosion problem for model checking, but are restricted by the hard problem of determining equivalence of states during search. Consequently, existing symmetry reduction packages can only exploit full symmetry between system components, as checking the equivalence of states is straightforward in this special case. We present a framework for symmetry reduction with an arbitrary group of structural symmetries. By generalising existing techniques for efficiently exploiting symmetry, and introducing an approximate strategy for use with groups for which fast, exact strategies are not available, our approach allows for significant state-space reduction with minimal time overhead. We show how computational group theoretic techniques can be used to analyse the structure of a symmetry group so that an appropriate symmetry reduction strategy can be chosen, and we describe a symmetry reduction package for the SPIN model checker which interfaces with the computational algebra system GAP. Experimental results on a variety of Promela models illustrate the effectiveness of our methods

A computational group theoretic symmetry reduction package for the SPIN model checker

Symmetry reduced model checking is hindered by two problems: how to identify state space symmetry when systems are not fully symmetric, and how to determine equivalence of states during search. We present TopSpin, a fully automatic symmetry reduction package for the Spin model checker. TopSpin uses the Gap computational algebra system to effectively detect state space symmetry from the associated Promela specification, and to choose an efficient symmetry reduction strategy by classifying automorphism groups as a disjoint/wreath product of subgroups. We present encouraging experimental results for a variety of Promela examples

Learning and Designing Stochastic Processes from Logical Constraints

Stochastic processes offer a flexible mathematical formalism to model and reason about systems. Most analysis tools, however, start from the premises that models are fully specified, so that any parameters controlling the system's dynamics must be known exactly. As this is seldom the case, many methods have been devised over the last decade to infer (learn) such parameters from observations of the state of the system. In this paper, we depart from this approach by assuming that our observations are {\it qualitative} properties encoded as satisfaction of linear temporal logic formulae, as opposed to quantitative observations of the state of the system. An important feature of this approach is that it unifies naturally the system identification and the system design problems, where the properties, instead of observations, represent requirements to be satisfied. We develop a principled statistical estimation procedure based on maximising the likelihood of the system's parameters, using recent ideas from statistical machine learning. We demonstrate the efficacy and broad applicability of our method on a range of simple but non-trivial examples, including rumour spreading in social networks and hybrid models of gene regulation

Automatic techniques for detecting and exploiting symmetry in model checking

The application of model checking is limited due to the state-space explosion problem – as the number of components represented by a model increase, the worst case size of the associated state-space grows exponentially. Current techniques can handle limited kinds of symmetry, e.g. full symmetry between identical components in a concurrent system. They avoid the problem of automatic symmetry detection by requiring the user to specify the presence of symmetry in a model (explicitly, or by annotating the associated specification using additional language keywords), or by restricting the input language of a model checker so that only symmetric systems can be specified. Additionally, computing unique representatives for each symmetric equivalence class is easy for these limited kinds of symmetry. We present a theoretical framework for symmetry reduction which can be applied to explicit state model checking. The framework includes techniques for automatic symmetry detection using computational group theory, which can be applied with no additional user input. These techniques detect structural symmetries induced by the topology of a concurrent system, so our framework includes exact and approximate techniques to efficiently exploit arbitrary symmetry groups which may arise in this way. These techniques are also based on computational group theoretic methods. We prove that our framework is logically sound, and demonstrate its general applicability to explicit state model checking. By providing a new symmetry reduction package for the SPIN model checker, we show that our framework can be feasibly implemented as part of a system which is widely used in both industry and academia. Through a study of SPIN users, we assess the usability of our automatic symmetry detection techniques in practice

Verification and Control of Partially Observable Probabilistic Real-Time Systems

We propose automated techniques for the verification and control of probabilistic real-time systems that are only partially observable. To formally model such systems, we define an extension of probabilistic timed automata in which local states are partially visible to an observer or controller. We give a probabilistic temporal logic that can express a range of quantitative properties of these models, relating to the probability of an event's occurrence or the expected value of a reward measure. We then propose techniques to either verify that such a property holds or to synthesise a controller for the model which makes it true. Our approach is based on an integer discretisation of the model's dense-time behaviour and a grid-based abstraction of the uncountable belief space induced by partial observability. The latter is necessarily approximate since the underlying problem is undecidable, however we show how both lower and upper bounds on numerical results can be generated. We illustrate the effectiveness of the approach by implementing it in the PRISM model checker and applying it to several case studies, from the domains of computer security and task scheduling