234,822 research outputs found
Formalising the Continuous/Discrete Modeling Step
Formally capturing the transition from a continuous model to a discrete model
is investigated using model based refinement techniques. A very simple model
for stopping (eg. of a train) is developed in both the continuous and discrete
domains. The difference between the two is quantified using generic results
from ODE theory, and these estimates can be compared with the exact solutions.
Such results do not fit well into a conventional model based refinement
framework; however they can be accommodated into a model based retrenchment.
The retrenchment is described, and the way it can interface to refinement
development on both the continuous and discrete sides is outlined. The approach
is compared to what can be achieved using hybrid systems techniques.Comment: In Proceedings Refine 2011, arXiv:1106.348
Collaborative Verification-Driven Engineering of Hybrid Systems
Hybrid systems with both discrete and continuous dynamics are an important
model for real-world cyber-physical systems. The key challenge is to ensure
their correct functioning w.r.t. safety requirements. Promising techniques to
ensure safety seem to be model-driven engineering to develop hybrid systems in
a well-defined and traceable manner, and formal verification to prove their
correctness. Their combination forms the vision of verification-driven
engineering. Often, hybrid systems are rather complex in that they require
expertise from many domains (e.g., robotics, control systems, computer science,
software engineering, and mechanical engineering). Moreover, despite the
remarkable progress in automating formal verification of hybrid systems, the
construction of proofs of complex systems often requires nontrivial human
guidance, since hybrid systems verification tools solve undecidable problems.
It is, thus, not uncommon for development and verification teams to consist of
many players with diverse expertise. This paper introduces a
verification-driven engineering toolset that extends our previous work on
hybrid and arithmetic verification with tools for (i) graphical (UML) and
textual modeling of hybrid systems, (ii) exchanging and comparing models and
proofs, and (iii) managing verification tasks. This toolset makes it easier to
tackle large-scale verification tasks
Workshop on Verification and Theorem Proving for Continuous Systems (NetCA Workshop 2005)
Oxford, UK, 26 August 200
Formal Verification of Full-Wave Rectifier: A Case Study
We present a case study of formal verification of full-wave rectifier for
analog and mixed signal designs. We have used the Checkmate tool from CMU [1],
which is a public domain formal verification tool for hybrid systems. Due to
the restriction imposed by Checkmate it necessitates to make the changes in the
Checkmate implementation to implement the complex and non-linear system.
Full-wave rectifier has been implemented by using the Checkmate custom blocks
and the Simulink blocks from MATLAB from Math works. After establishing the
required changes in the Checkmate implementation we are able to efficiently
verify the safety properties of the full-wave rectifier.Comment: The IEEE 8th International Conference on ASIC (IEEE ASICON 2009),
October 20-23 2009, Changsha, Chin
StocHy: automated verification and synthesis of stochastic processes
StocHy is a software tool for the quantitative analysis of discrete-time
stochastic hybrid systems (SHS). StocHy accepts a high-level description of
stochastic models and constructs an equivalent SHS model. The tool allows to
(i) simulate the SHS evolution over a given time horizon; and to automatically
construct formal abstractions of the SHS. Abstractions are then employed for
(ii) formal verification or (iii) control (policy, strategy) synthesis. StocHy
allows for modular modelling, and has separate simulation, verification and
synthesis engines, which are implemented as independent libraries. This allows
for libraries to be easily used and for extensions to be easily built. The tool
is implemented in C++ and employs manipulations based on vector calculus, the
use of sparse matrices, the symbolic construction of probabilistic kernels, and
multi-threading. Experiments show StocHy's markedly improved performance when
compared to existing abstraction-based approaches: in particular, StocHy beats
state-of-the-art tools in terms of precision (abstraction error) and
computational effort, and finally attains scalability to large-sized models (12
continuous dimensions). StocHy is available at www.gitlab.com/natchi92/StocHy
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Using formal methods to support testing
Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent
years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing
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