3,684 research outputs found
Learning to Prove Safety over Parameterised Concurrent Systems (Full Version)
We revisit the classic problem of proving safety over parameterised
concurrent systems, i.e., an infinite family of finite-state concurrent systems
that are represented by some finite (symbolic) means. An example of such an
infinite family is a dining philosopher protocol with any number n of processes
(n being the parameter that defines the infinite family). Regular model
checking is a well-known generic framework for modelling parameterised
concurrent systems, where an infinite set of configurations (resp. transitions)
is represented by a regular set (resp. regular transducer). Although verifying
safety properties in the regular model checking framework is undecidable in
general, many sophisticated semi-algorithms have been developed in the past
fifteen years that can successfully prove safety in many practical instances.
In this paper, we propose a simple solution to synthesise regular inductive
invariants that makes use of Angluin's classic L* algorithm (and its variants).
We provide a termination guarantee when the set of configurations reachable
from a given set of initial configurations is regular. We have tested L*
algorithm on standard (as well as new) examples in regular model checking
including the dining philosopher protocol, the dining cryptographer protocol,
and several mutual exclusion protocols (e.g. Bakery, Burns, Szymanski, and
German). Our experiments show that, despite the simplicity of our solution, it
can perform at least as well as existing semi-algorithms.Comment: Full version of FMCAD'17 pape
Tree Regular Model Checking for Lattice-Based Automata
Tree Regular Model Checking (TRMC) is the name of a family of techniques for
analyzing infinite-state systems in which states are represented by terms, and
sets of states by Tree Automata (TA). The central problem in TRMC is to decide
whether a set of bad states is reachable. The problem of computing a TA
representing (an over- approximation of) the set of reachable states is
undecidable, but efficient solutions based on completion or iteration of tree
transducers exist. Unfortunately, the TRMC framework is unable to efficiently
capture both the complex structure of a system and of some of its features. As
an example, for JAVA programs, the structure of a term is mainly exploited to
capture the structure of a state of the system. On the counter part, integers
of the java programs have to be encoded with Peano numbers, which means that
any algebraic operation is potentially represented by thousands of applications
of rewriting rules. In this paper, we propose Lattice Tree Automata (LTAs), an
extended version of tree automata whose leaves are equipped with lattices. LTAs
allow us to represent possibly infinite sets of interpreted terms. Such terms
are capable to represent complex domains and related operations in an efficient
manner. We also extend classical Boolean operations to LTAs. Finally, as a
major contribution, we introduce a new completion-based algorithm for computing
the possibly infinite set of reachable interpreted terms in a finite amount of
time.Comment: Technical repor
Enhancing Approximations for Regular Reachability Analysis
This paper introduces two mechanisms for computing over-approximations of
sets of reachable states, with the aim of ensuring termination of state-space
exploration. The first mechanism consists in over-approximating the automata
representing reachable sets by merging some of their states with respect to
simple syntactic criteria, or a combination of such criteria. The second
approximation mechanism consists in manipulating an auxiliary automaton when
applying a transducer representing the transition relation to an automaton
encoding the initial states. In addition, for the second mechanism we propose a
new approach to refine the approximations depending on a property of interest.
The proposals are evaluated on examples of mutual exclusion protocols
Stream Processing using Grammars and Regular Expressions
In this dissertation we study regular expression based parsing and the use of
grammatical specifications for the synthesis of fast, streaming
string-processing programs.
In the first part we develop two linear-time algorithms for regular
expression based parsing with Perl-style greedy disambiguation. The first
algorithm operates in two passes in a semi-streaming fashion, using a constant
amount of working memory and an auxiliary tape storage which is written in the
first pass and consumed by the second. The second algorithm is a single-pass
and optimally streaming algorithm which outputs as much of the parse tree as is
semantically possible based on the input prefix read so far, and resorts to
buffering as many symbols as is required to resolve the next choice. Optimality
is obtained by performing a PSPACE-complete pre-analysis on the regular
expression.
In the second part we present Kleenex, a language for expressing
high-performance streaming string processing programs as regular grammars with
embedded semantic actions, and its compilation to streaming string transducers
with worst-case linear-time performance. Its underlying theory is based on
transducer decomposition into oracle and action machines, and a finite-state
specialization of the streaming parsing algorithm presented in the first part.
In the second part we also develop a new linear-time streaming parsing
algorithm for parsing expression grammars (PEG) which generalizes the regular
grammars of Kleenex. The algorithm is based on a bottom-up tabulation algorithm
reformulated using least fixed points and evaluated using an instance of the
chaotic iteration scheme by Cousot and Cousot
Active learning and the Irish treebank
We report on our ongoing work in developing the Irish Dependency Treebank, describe the results of two Inter annotator Agreement (IAA) studies, demonstrate improvements in annotation consistency which have a knock-on effect on parsing accuracy, and present the final set of dependency labels. We then go on to investigate the extent to which active learning can play a role in treebank and parser development by comparing an active learning bootstrapping approach to a passive approach in which sentences are chosen at random for manual revision. We show that active learning outperforms passive learning, but when annotation effort is taken into account, it is not clear how much of an advantage the active learning approach has. Finally, we present results which suggest that adding automatic parses to the training data along with manually revised parses in an active learning setup does not greatly affect parsing accuracy
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