Sergiy Bogomolov

HYST: a source transformation and translation tool for hybrid automaton models

A number of powerful and scalable hybrid systems model checkers have recently emerged. Although all of them honor roughly the same hybrid systems semantics, they have drastically different model description languages. This situation (a) makes it difficult to quickly evaluate a specific hybrid automaton model using the different tools, (b) obstructs comparisons of reachability approaches, and (c) impedes the widespread application of research results that perform model modification and could benefit many of the tools. In this paper, we present Hyst, a Hybrid Source Transformer. Hyst is a source-to-source translation tool, currently taking input in the SpaceEx model format, and translating to the formats of HyCreate, Flow*, or dReach. Internally, the tool supports generic model-to-model transformation passes that serve to both ease the translation and potentially improve reachability results for the supported tools. Although these model transformation passes could be implemented within each tool, the Hyst approach provides a single place for model modification, generating modified input sources for the unmodified target tools. Our evaluation demonstrates Hyst is capable of automatically translating benchmarks in several classes (including affine and nonlinear hybrid automata) to the input formats of several tools. Additionally, we illustrate a general model transformation pass based on pseudo-invariants implemented in Hyst that illustrates the reachability improvement.

Assume-Guarantee Abstraction Refinement Meets Hybrid Systems

Compositional verification techniques in the assume-guarantee style have been successfully applied to transition systems to efficiently reduce the search space by leveraging the compositional nature of the systems under consideration. We adapt these techniques to the domain of hybrid systems with affine dynamics. To build assumptions we introduce an abstraction based on location merging. We integrate the assume-guarantee style analysis with automatic abstraction refinement. We have implemented our approach in the symbolic hybrid model checker SpaceEx. The evaluation shows its practical potential. To the best of our knowledge, this is the first work combining assume-guarantee reasoning with automatic abstraction-refinement in the context of hybrid automata.

Guided search for hybrid systems based on coarse-grained space abstractions

Hybrid systems represent an important and powerful formalism for modeling real-world applications such as embedded systems. A verification tool like SpaceEx is based on the exploration of a symbolic search space (the region space). As a verification tool, it is typically optimized towards proving the absence of errors. In some settings, e.g., when the verification tool is employed in a feedback-directed design cycle, one would like to have the option to call a version that is optimized towards finding an error trajectory in the region space. A recent approach in this direction is based on guided search. Guided search relies on a cost function that indicates which states are promising to be explored, and preferably explores more promising states first. In this paper, we propose an abstraction-based cost function based on coarse-grained space abstractions for guiding the reachability analysis. For this purpose, a suitable abstraction technique that exploits the flexible granularity of modern reachability analysis algorithms is introduced. The new cost function is an effective extension of pattern database approaches that have been successfully applied in other areas. The approach has been implemented in the SpaceEx model checker. The evaluation shows its practical potential.

Abstraction-Based Parameter Synthesis for Multiaffine Systems

Multiaffine hybrid automata (MHA) represent a powerful formalism to model complex dynamical systems. This formalism is particularly suited for the representation of biological systems which often exhibit highly non-linear behavior. In this paper, we consider the problem of parameter identification for MHA. We present an abstraction of MHA based on linear hybrid automata, which can be analyzed by the SpaceEx model checker. This abstraction enables a precise handling of time-dependent properties. We demonstrate the potential of our approach on a model of a genetic regulatory network and a myocyte model.

Abstraction-based guided search for hybrid systems

Hybrid systems represent an important and powerful formalism for modeling real-world applications such as embedded systems. A verification tool like SpaceEx is based on the exploration of a symbolic search space (the region space). As a verification tool, it is typically optimized towards proving the absence of errors. In some settings, e.g., when the verification tool is employed in a feedback-directed design cycle, one would like to have the option to call a version that is optimized towards finding an error path in the region space. A recent approach in this direction is based on guided search. Guided search relies on a cost function that indicates which states are promising to be explored, and preferably explores more promising states first. In this paper, an abstraction-based cost function based on pattern databases for guiding the reachability analysis is proposed. For this purpose, a suitable abstraction technique that exploits the flexible granularity of modern reachability analysis algorithms is introduced. The new cost function is an effective extension of pattern database approaches that have been successfully applied in other areas. The approach has been implemented in the SpaceEx model checker. The evaluation shows its practical potential.

A box-based distance between regions for guiding the reachability analysis of spaceex

A recent technique used in falsification methods for hybrid systems relies on distance-based heuristics for guiding the search towards a goal state. The question is whether the technique can be carried over to reachability analyses that use regions as their basic data structure. In this paper, we introduce a box-based distance measure between regions. We present an algorithm that, given two regions, efficiently computes the box-based distance between them. We have implemented the algorithm in SpaceEx and use it for guiding the region-based reachability analysis of SpaceEx. We illustrate the practical potential of our approach in a case study for the navigation benchmark.

Eliminating spurious transitions in reachability with support functions

Computing an approximation of the reachable states of a hybrid system is a challenge, mainly because overapproximating the solutions of ODEs with a finite number of sets does not scale well. Using template polyhedra can greatly reduce the computational complexity, since it replaces complex operations on sets with a small number of optimization problems. However, the use of templates may make the over-approximation too conservative. Spurious transitions, which are falsely considered reachable, are particularly detrimental to performance and accuracy, and may exacerbate the state explosion problem. In this paper, we examine how spurious transitions can be avoided with minimal computational effort. To this end, detecting spurious transitions is reduced to the well-known problem of showing that two convex sets are disjoint by finding a hyperplane that separates them. We generalize this to flowpipes by considering hyperplanes that evolve with time in correspondence to the dynamics of the system. The approach is implemented in the model checker SpaceEx and demonstrated on examples.

Composing reachability analyses of hybrid systems for safety and stability

We present a method to enhance the power of a given reachability analysis engine for hybrid systems. The method works by a new form of composition of reachability analyses, each on a different relaxation of the input hybrid system. We present preliminary experiments that indicate its practical potential for checking safety and stability.

Counterexample-Guided Refinement of Template Polyhedra

Template polyhedra generalize intervals and octagons to polyhedra whose facets are orthogonal to a given set of arbitrary directions. They have been employed in the abstract interpretation of programs and, with particular success, in the reachability analysis of hybrid automata. While previously, the choice of directions has been left to the user or a heuristic, we present a method for the automatic discovery of directions that generalize and eliminate spurious counterexamples. We show that for the class of convex hybrid automata, i.e., hybrid automata with (possibly nonlinear) convex constraints on derivatives, such directions always exist and can be found using convex optimization. We embed our method inside a CEGAR loop, thus enabling the time-unbounded reachability analysis of an important and richer class of hybrid automata than was previously possible. We evaluate our method on several benchmarks, demonstrating also its superior efficiency for the special case of linear hybrid automata.

Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks

Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space.