29 research outputs found
Synthesis of Recursive ADT Transformations from Reusable Templates
Recent work has proposed a promising approach to improving scalability of
program synthesis by allowing the user to supply a syntactic template that
constrains the space of potential programs. Unfortunately, creating templates
often requires nontrivial effort from the user, which impedes the usability of
the synthesizer. We present a solution to this problem in the context of
recursive transformations on algebraic data-types. Our approach relies on
polymorphic synthesis constructs: a small but powerful extension to the
language of syntactic templates, which makes it possible to define a program
space in a concise and highly reusable manner, while at the same time retains
the scalability benefits of conventional templates. This approach enables
end-users to reuse predefined templates from a library for a wide variety of
problems with little effort. The paper also describes a novel optimization that
further improves the performance and scalability of the system. We evaluated
the approach on a set of benchmarks that most notably includes desugaring
functions for lambda calculus, which force the synthesizer to discover Church
encodings for pairs and boolean operations
On Counterexample Guided Quantifier Instantiation for Synthesis in CVC4
We introduce the first program synthesis engine implemented inside an SMT
solver. We present an approach that extracts solution functions from
unsatisfiability proofs of the negated form of synthesis conjectures. We also
discuss novel counterexample-guided techniques for quantifier instantiation
that we use to make finding such proofs practically feasible. A particularly
important class of specifications are single-invocation properties, for which
we present a dedicated algorithm. To support syntax restrictions on generated
solutions, our approach can transform a solution found without restrictions
into the desired syntactic form. As an alternative, we show how to use
evaluation function axioms to embed syntactic restrictions into constraints
over algebraic datatypes, and then use an algebraic datatype decision procedure
to drive synthesis. Our experimental evaluation on syntax-guided synthesis
benchmarks shows that our implementation in the CVC4 SMT solver is competitive
with state-of-the-art tools for synthesis
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
Static versus Dynamic Verification in Why3, Frama-C and SPARK 2014
International audienceWhy3 is an environment for static verification, generic in the sense that it is used as an intermediate tool by different front-ends for the verification of Java, C or Ada programs. Yet, the choices made when designing the specification languages provided by those front-ends differ significantly, in particular with respect to the executability of specifications. We review these differences and the issues that result from these choices. We emphasize the specific feature of ghost code which turns out to be extremely useful for both static and dynamic verification. We also present techniques, combining static and dynamic features, that help users understand why static verification fails