7,819 research outputs found
Generalizing the Paige-Tarjan Algorithm by Abstract Interpretation
The Paige and Tarjan algorithm (PT) for computing the coarsest refinement of
a state partition which is a bisimulation on some Kripke structure is well
known. It is also well known in model checking that bisimulation is equivalent
to strong preservation of CTL, or, equivalently, of Hennessy-Milner logic.
Drawing on these observations, we analyze the basic steps of the PT algorithm
from an abstract interpretation perspective, which allows us to reason on
strong preservation in the context of generic inductively defined (temporal)
languages and of possibly non-partitioning abstract models specified by
abstract interpretation. This leads us to design a generalized Paige-Tarjan
algorithm, called GPT, for computing the minimal refinement of an abstract
interpretation-based model that strongly preserves some given language. It
turns out that PT is a straight instance of GPT on the domain of state
partitions for the case of strong preservation of Hennessy-Milner logic. We
provide a number of examples showing that GPT is of general use. We first show
how a well-known efficient algorithm for computing stuttering equivalence can
be viewed as a simple instance of GPT. We then instantiate GPT in order to
design a new efficient algorithm for computing simulation equivalence that is
competitive with the best available algorithms. Finally, we show how GPT allows
to compute new strongly preserving abstract models by providing an efficient
algorithm that computes the coarsest refinement of a given partition that
strongly preserves the language generated by the reachability operator.Comment: Keywords: Abstract interpretation, abstract model checking, strong
preservation, Paige-Tarjan algorithm, refinement algorith
Generalized Strong Preservation by Abstract Interpretation
Standard abstract model checking relies on abstract Kripke structures which
approximate concrete models by gluing together indistinguishable states, namely
by a partition of the concrete state space. Strong preservation for a
specification language L encodes the equivalence of concrete and abstract model
checking of formulas in L. We show how abstract interpretation can be used to
design abstract models that are more general than abstract Kripke structures.
Accordingly, strong preservation is generalized to abstract
interpretation-based models and precisely related to the concept of
completeness in abstract interpretation. The problem of minimally refining an
abstract model in order to make it strongly preserving for some language L can
be formulated as a minimal domain refinement in abstract interpretation in
order to get completeness w.r.t. the logical/temporal operators of L. It turns
out that this refined strongly preserving abstract model always exists and can
be characterized as a greatest fixed point. As a consequence, some well-known
behavioural equivalences, like bisimulation, simulation and stuttering, and
their corresponding partition refinement algorithms can be elegantly
characterized in abstract interpretation as completeness properties and
refinements
Language-based Abstractions for Dynamical Systems
Ordinary differential equations (ODEs) are the primary means to modelling
dynamical systems in many natural and engineering sciences. The number of
equations required to describe a system with high heterogeneity limits our
capability of effectively performing analyses. This has motivated a large body
of research, across many disciplines, into abstraction techniques that provide
smaller ODE systems while preserving the original dynamics in some appropriate
sense. In this paper we give an overview of a recently proposed
computer-science perspective to this problem, where ODE reduction is recast to
finding an appropriate equivalence relation over ODE variables, akin to
classical models of computation based on labelled transition systems.Comment: In Proceedings QAPL 2017, arXiv:1707.0366
Lazy Abstraction-Based Controller Synthesis
We present lazy abstraction-based controller synthesis (ABCS) for
continuous-time nonlinear dynamical systems against reach-avoid and safety
specifications. State-of-the-art multi-layered ABCS pre-computes multiple
finite-state abstractions of varying granularity and applies reactive synthesis
to the coarsest abstraction whenever feasible, but adaptively considers finer
abstractions when necessary. Lazy ABCS improves this technique by constructing
abstractions on demand. Our insight is that the abstract transition relation
only needs to be locally computed for a small set of frontier states at the
precision currently required by the synthesis algorithm. We show that lazy ABCS
can significantly outperform previous multi-layered ABCS algorithms: on
standard benchmarks, lazy ABCS is more than 4 times faster
Path-Based Program Repair
We propose a path-based approach to program repair for imperative programs.
Our repair framework takes as input a faulty program, a logic specification
that is refuted, and a hint where the fault may be located. An iterative
abstraction refinement loop is then used to repair the program: in each
iteration, the faulty program part is re-synthesized considering a symbolic
counterexample, where the control-flow is kept concrete but the data-flow is
symbolic. The appeal of the idea is two-fold: 1) the approach lazily considers
candidate repairs and 2) the repairs are directly derived from the logic
specification. In contrast to prior work, our approach is complete for programs
with finitely many control-flow paths, i.e., the program is repaired if and
only if it can be repaired at the specified fault location. Initial results for
small programs indicate that the approach is useful for debugging programs in
practice.Comment: In Proceedings FESCA 2015, arXiv:1503.0437
Challenges in Quantitative Abstractions for Collective Adaptive Systems
Like with most large-scale systems, the evaluation of quantitative properties
of collective adaptive systems is an important issue that crosscuts all its
development stages, from design (in the case of engineered systems) to runtime
monitoring and control. Unfortunately it is a difficult problem to tackle in
general, due to the typically high computational cost involved in the analysis.
This calls for the development of appropriate quantitative abstraction
techniques that preserve most of the system's dynamical behaviour using a more
compact representation. This paper focuses on models based on ordinary
differential equations and reviews recent results where abstraction is achieved
by aggregation of variables, reflecting on the shortcomings in the state of the
art and setting out challenges for future research.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200
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