858 research outputs found
{SAT} Solvers for Queries over Tree Automata with Constraints
International audienceTree automata turned out to be a very convenient framework for modeling and proving properties on infinite systems like communication protocols, Java programs and also in the context of XML programming. Unfortunately, these works are not always supported by efficient verification and validation tools. This paper investigates the use of two SAT solvers --- minisat and picosat--- to evaluate queries over tree automata with global equality and disequality constraints (TAGED s for short). Unlike general tree automata, TAGED s allow to express constraints useful for e.g., evaluating queries over XML documents, like "in the document, two nodes do not have the same key". These queries being based on the membership problem which is NP-complete for TAGEDs, we propose an efficient SAT encoding of the membership problem for TAGEDs and we show its correctness and soundness. The paper reports on the experimental results, and implementation details are given
Abstract Learning Frameworks for Synthesis
We develop abstract learning frameworks (ALFs) for synthesis that embody the
principles of CEGIS (counter-example based inductive synthesis) strategies that
have become widely applicable in recent years. Our framework defines a general
abstract framework of iterative learning, based on a hypothesis space that
captures the synthesized objects, a sample space that forms the space on which
induction is performed, and a concept space that abstractly defines the
semantics of the learning process. We show that a variety of synthesis
algorithms in current literature can be embedded in this general framework.
While studying these embeddings, we also generalize some of the synthesis
problems these instances are of, resulting in new ways of looking at synthesis
problems using learning. We also investigate convergence issues for the general
framework, and exhibit three recipes for convergence in finite time. The first
two recipes generalize current techniques for convergence used by existing
synthesis engines. The third technique is a more involved technique of which we
know of no existing instantiation, and we instantiate it to concrete synthesis
problems
Learning Concise Models from Long Execution Traces
Abstract models of system-level behaviour have applications in design
exploration, analysis, testing and verification. We describe a new algorithm
for automatically extracting useful models, as automata, from execution traces
of a HW/SW system driven by software exercising a use-case of interest. Our
algorithm leverages modern program synthesis techniques to generate predicates
on automaton edges, succinctly describing system behaviour. It employs trace
segmentation to tackle complexity for long traces. We learn concise models
capturing transaction-level, system-wide behaviour--experimentally
demonstrating the approach using traces from a variety of sources, including
the x86 QEMU virtual platform and the Real-Time Linux kernel
Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis
Even with impressive advances in automated formal methods, certain problems
in system verification and synthesis remain challenging. Examples include the
verification of quantitative properties of software involving constraints on
timing and energy consumption, and the automatic synthesis of systems from
specifications. The major challenges include environment modeling,
incompleteness in specifications, and the complexity of underlying decision
problems.
This position paper proposes sciduction, an approach to tackle these
challenges by integrating inductive inference, deductive reasoning, and
structure hypotheses. Deductive reasoning, which leads from general rules or
concepts to conclusions about specific problem instances, includes techniques
such as logical inference and constraint solving. Inductive inference, which
generalizes from specific instances to yield a concept, includes algorithmic
learning from examples. Structure hypotheses are used to define the class of
artifacts, such as invariants or program fragments, generated during
verification or synthesis. Sciduction constrains inductive and deductive
reasoning using structure hypotheses, and actively combines inductive and
deductive reasoning: for instance, deductive techniques generate examples for
learning, and inductive reasoning is used to guide the deductive engines.
We illustrate this approach with three applications: (i) timing analysis of
software; (ii) synthesis of loop-free programs, and (iii) controller synthesis
for hybrid systems. Some future applications are also discussed
Learning-Based Synthesis of Safety Controllers
We propose a machine learning framework to synthesize reactive controllers
for systems whose interactions with their adversarial environment are modeled
by infinite-duration, two-player games over (potentially) infinite graphs. Our
framework targets safety games with infinitely many vertices, but it is also
applicable to safety games over finite graphs whose size is too prohibitive for
conventional synthesis techniques. The learning takes place in a feedback loop
between a teacher component, which can reason symbolically about the safety
game, and a learning algorithm, which successively learns an overapproximation
of the winning region from various kinds of examples provided by the teacher.
We develop a novel decision tree learning algorithm for this setting and show
that our algorithm is guaranteed to converge to a reactive safety controller if
a suitable overapproximation of the winning region can be expressed as a
decision tree. Finally, we empirically compare the performance of a prototype
implementation to existing approaches, which are based on constraint solving
and automata learning, respectively
08171 Abstracts Collection -- Beyond the Finite: New Challenges in Verification and Semistructured Data
From 20.04. to 25.04.2008, the Dagstuhl Seminar 08171 ``Beyond the Finite: New Challenges in Verification and Semistructured Data\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Minimal Synthesis of String To String Functions From Examples
We study the problem of synthesizing string to string transformations from a
set of input/output examples. The transformations we consider are expressed
using deterministic finite automata (DFA) that read pairs of letters, one
letter from the input and one from the output. The DFA corresponding to these
transformations have additional constraints, ensuring that each input string is
mapped to exactly one output string.
We suggest that, given a set of input/output examples, the smallest DFA
consistent with the examples is a good candidate for the transformation the
user was expecting. We therefore study the problem of, given a set of examples,
finding a minimal DFA consistent with the examples and satisfying the
functionality and totality constraints mentioned above.
We prove that, in general, this problem (the corresponding decision problem)
is NP-complete. This is unlike the standard DFA minimization problem which can
be solved in polynomial time. We provide several NP-hardness proofs that show
the hardness of multiple (independent) variants of the problem.
Finally, we propose an algorithm for finding the minimal DFA consistent with
input/output examples, that uses a reduction to SMT solvers. We implemented the
algorithm, and used it to evaluate the likelihood that the minimal DFA indeed
corresponds to the DFA expected by the user.Comment: SYNT 201
Learning Linear Temporal Properties
We present two novel algorithms for learning formulas in Linear Temporal
Logic (LTL) from examples. The first learning algorithm reduces the learning
task to a series of satisfiability problems in propositional Boolean logic and
produces a smallest LTL formula (in terms of the number of subformulas) that is
consistent with the given data. Our second learning algorithm, on the other
hand, combines the SAT-based learning algorithm with classical algorithms for
learning decision trees. The result is a learning algorithm that scales to
real-world scenarios with hundreds of examples, but can no longer guarantee to
produce minimal consistent LTL formulas. We compare both learning algorithms
and demonstrate their performance on a wide range of synthetic benchmarks.
Additionally, we illustrate their usefulness on the task of understanding
executions of a leader election protocol
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