12,875 research outputs found
Invariant Synthesis for Incomplete Verification Engines
We propose a framework for synthesizing inductive invariants for incomplete
verification engines, which soundly reduce logical problems in undecidable
theories to decidable theories. Our framework is based on the counter-example
guided inductive synthesis principle (CEGIS) and allows verification engines to
communicate non-provability information to guide invariant synthesis. We show
precisely how the verification engine can compute such non-provability
information and how to build effective learning algorithms when invariants are
expressed as Boolean combinations of a fixed set of predicates. Moreover, we
evaluate our framework in two verification settings, one in which verification
engines need to handle quantified formulas and one in which verification
engines have to reason about heap properties expressed in an expressive but
undecidable separation logic. Our experiments show that our invariant synthesis
framework based on non-provability information can both effectively synthesize
inductive invariants and adequately strengthen contracts across a large suite
of programs
Synthesizing Short-Circuiting Validation of Data Structure Invariants
This paper presents incremental verification-validation, a novel approach for
checking rich data structure invariants expressed as separation logic
assertions. Incremental verification-validation combines static verification of
separation properties with efficient, short-circuiting dynamic validation of
arbitrarily rich data constraints. A data structure invariant checker is an
inductive predicate in separation logic with an executable interpretation; a
short-circuiting checker is an invariant checker that stops checking whenever
it detects at run time that an assertion for some sub-structure has been fully
proven statically. At a high level, our approach does two things: it statically
proves the separation properties of data structure invariants using a static
shape analysis in a standard way but then leverages this proof in a novel
manner to synthesize short-circuiting dynamic validation of the data
properties. As a consequence, we enable dynamic validation to make up for
imprecision in sound static analysis while simultaneously leveraging the static
verification to make the remaining dynamic validation efficient. We show
empirically that short-circuiting can yield asymptotic improvements in dynamic
validation, with low overhead over no validation, even in cases where static
verification is incomplete
On Deciding Local Theory Extensions via E-matching
Satisfiability Modulo Theories (SMT) solvers incorporate decision procedures
for theories of data types that commonly occur in software. This makes them
important tools for automating verification problems. A limitation frequently
encountered is that verification problems are often not fully expressible in
the theories supported natively by the solvers. Many solvers allow the
specification of application-specific theories as quantified axioms, but their
handling is incomplete outside of narrow special cases.
In this work, we show how SMT solvers can be used to obtain complete decision
procedures for local theory extensions, an important class of theories that are
decidable using finite instantiation of axioms. We present an algorithm that
uses E-matching to generate instances incrementally during the search,
significantly reducing the number of generated instances compared to eager
instantiation strategies. We have used two SMT solvers to implement this
algorithm and conducted an extensive experimental evaluation on benchmarks
derived from verification conditions for heap-manipulating programs. We believe
that our results are of interest to both the users of SMT solvers as well as
their developers
Aircraft systems architecting: a functional-logical domain perspective
Presented is a novel framework for early systems architecture design. The framework defines data structures and algorithms that enable the systems architect to operate interactively and simultaneously in both the functional and logical domains. A prototype software tool, called AirCADia Architect, was implemented, which allowed the framework to be evaluated by practicing aircraft systems architects. The evaluation confirmed that, on the whole, the approach enables the architects to effectively express their creative ideas when synthesizing new architectures while still retaining control over the process
Symbolic and analytic techniques for resource analysis of Java bytecode
Recent work in resource analysis has translated the idea of amortised resource analysis to imperative languages using a program logic that allows mixing of assertions about heap shapes, in the tradition of separation logic, and assertions about consumable resources. Separately, polyhedral methods have been used to calculate bounds on numbers of iterations in loop-based programs. We are attempting to combine these ideas to deal with Java programs involving both data structures and loops, focusing on the bytecode level rather than on source code
From Uncertainty Data to Robust Policies for Temporal Logic Planning
We consider the problem of synthesizing robust disturbance feedback policies
for systems performing complex tasks. We formulate the tasks as linear temporal
logic specifications and encode them into an optimization framework via
mixed-integer constraints. Both the system dynamics and the specifications are
known but affected by uncertainty. The distribution of the uncertainty is
unknown, however realizations can be obtained. We introduce a data-driven
approach where the constraints are fulfilled for a set of realizations and
provide probabilistic generalization guarantees as a function of the number of
considered realizations. We use separate chance constraints for the
satisfaction of the specification and operational constraints. This allows us
to quantify their violation probabilities independently. We compute disturbance
feedback policies as solutions of mixed-integer linear or quadratic
optimization problems. By using feedback we can exploit information of past
realizations and provide feasibility for a wider range of situations compared
to static input sequences. We demonstrate the proposed method on two robust
motion-planning case studies for autonomous driving
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