6,928 research outputs found
Predicate Abstraction with Indexed Predicates
Predicate abstraction provides a powerful tool for verifying properties of
infinite-state systems using a combination of a decision procedure for a subset
of first-order logic and symbolic methods originally developed for finite-state
model checking. We consider models containing first-order state variables,
where the system state includes mutable functions and predicates. Such a model
can describe systems containing arbitrarily large memories, buffers, and arrays
of identical processes. We describe a form of predicate abstraction that
constructs a formula over a set of universally quantified variables to describe
invariant properties of the first-order state variables. We provide a formal
justification of the soundness of our approach and describe how it has been
used to verify several hardware and software designs, including a
directory-based cache coherence protocol.Comment: 27 pages, 4 figures, 1 table, short version appeared in International
Conference on Verification, Model Checking and Abstract Interpretation
(VMCAI'04), LNCS 2937, pages = 267--28
Parameterized Synthesis
We study the synthesis problem for distributed architectures with a
parametric number of finite-state components. Parameterized specifications
arise naturally in a synthesis setting, but thus far it was unclear how to
detect realizability and how to perform synthesis in a parameterized setting.
Using a classical result from verification, we show that for a class of
specifications in indexed LTL\X, parameterized synthesis in token ring networks
is equivalent to distributed synthesis in a network consisting of a few copies
of a single process. Adapting a well-known result from distributed synthesis,
we show that the latter problem is undecidable. We describe a semi-decision
procedure for the parameterized synthesis problem in token rings, based on
bounded synthesis. We extend the approach to parameterized synthesis in
token-passing networks with arbitrary topologies, and show applicability on a
simple case study. Finally, we sketch a general framework for parameterized
synthesis based on cutoffs and other parameterized verification techniques.Comment: Extended version of TACAS 2012 paper, 29 page
Sequentializing Parameterized Programs
We exhibit assertion-preserving (reachability preserving) transformations
from parameterized concurrent shared-memory programs, under a k-round
scheduling of processes, to sequential programs. The salient feature of the
sequential program is that it tracks the local variables of only one thread at
any point, and uses only O(k) copies of shared variables (it does not use extra
counters, not even one counter to keep track of the number of threads).
Sequentialization is achieved using the concept of a linear interface that
captures the effect an unbounded block of processes have on the shared state in
a k-round schedule. Our transformation utilizes linear interfaces to
sequentialize the program, and to ensure the sequential program explores only
reachable states and preserves local invariants.Comment: In Proceedings FIT 2012, arXiv:1207.348
BeSpaceD: Towards a Tool Framework and Methodology for the Specification and Verification of Spatial Behavior of Distributed Software Component Systems
In this report, we present work towards a framework for modeling and checking
behavior of spatially distributed component systems. Design goals of our
framework are the ability to model spatial behavior in a component oriented,
simple and intuitive way, the possibility to automatically analyse and verify
systems and integration possibilities with other modeling and verification
tools. We present examples and the verification steps necessary to prove
properties such as range coverage or the absence of collisions between
components and technical details
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
In recent years, the notion of local robustness (or robustness for short) has
emerged as a desirable property of deep neural networks. Intuitively,
robustness means that small perturbations to an input do not cause the network
to perform misclassifications. In this paper, we present a novel algorithm for
verifying robustness properties of neural networks. Our method synergistically
combines gradient-based optimization methods for counterexample search with
abstraction-based proof search to obtain a sound and ({\delta}-)complete
decision procedure. Our method also employs a data-driven approach to learn a
verification policy that guides abstract interpretation during proof search. We
have implemented the proposed approach in a tool called Charon and
experimentally evaluated it on hundreds of benchmarks. Our experiments show
that the proposed approach significantly outperforms three state-of-the-art
tools, namely AI^2 , Reluplex, and Reluval
Optimizing Abstract Abstract Machines
The technique of abstracting abstract machines (AAM) provides a systematic
approach for deriving computable approximations of evaluators that are easily
proved sound. This article contributes a complementary step-by-step process for
subsequently going from a naive analyzer derived under the AAM approach, to an
efficient and correct implementation. The end result of the process is a two to
three order-of-magnitude improvement over the systematically derived analyzer,
making it competitive with hand-optimized implementations that compute
fundamentally less precise results.Comment: Proceedings of the International Conference on Functional Programming
2013 (ICFP 2013). Boston, Massachusetts. September, 201
Rapid Recovery for Systems with Scarce Faults
Our goal is to achieve a high degree of fault tolerance through the control
of a safety critical systems. This reduces to solving a game between a
malicious environment that injects failures and a controller who tries to
establish a correct behavior. We suggest a new control objective for such
systems that offers a better balance between complexity and precision: we seek
systems that are k-resilient. In order to be k-resilient, a system needs to be
able to rapidly recover from a small number, up to k, of local faults
infinitely many times, provided that blocks of up to k faults are separated by
short recovery periods in which no fault occurs. k-resilience is a simple but
powerful abstraction from the precise distribution of local faults, but much
more refined than the traditional objective to maximize the number of local
faults. We argue why we believe this to be the right level of abstraction for
safety critical systems when local faults are few and far between. We show that
the computational complexity of constructing optimal control with respect to
resilience is low and demonstrate the feasibility through an implementation and
experimental results.Comment: In Proceedings GandALF 2012, arXiv:1210.202
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