6,335 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
A strategy for automatically generating programs in the lucid programming language
A strategy for automatically generating and verifying simple computer programs is described. The programs are specified by a precondition and a postcondition in predicate calculus. The programs generated are in the Lucid programming language, a high-level, data-flow language known for its attractive mathematical properties and ease of program verification. The Lucid programming is described, and the automatic program generation strategy is described and applied to several example problems
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