592 research outputs found
Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference
We address the predicate generation problem in the context of loop invariant
inference. Motivated by the interpolation-based abstraction refinement
technique, we apply the interpolation theorem to synthesize predicates
implicitly implied by program texts. Our technique is able to improve the
effectiveness and efficiency of the learning-based loop invariant inference
algorithm in [14]. We report experiment results of examples from Linux,
SPEC2000, and Tar utility
Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference
PETITION FOR ORIGINAL WRIT OF MANDAMUS DIRECTED TO THE HONORABLE DAVID L. MOWER DISTRICT JUDGE OF SEVIER COUNTY, STATE OF UTA
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
Spatial Interpolants
We propose Splinter, a new technique for proving properties of
heap-manipulating programs that marries (1) a new separation logic-based
analysis for heap reasoning with (2) an interpolation-based technique for
refining heap-shape invariants with data invariants. Splinter is property
directed, precise, and produces counterexample traces when a property does not
hold. Using the novel notion of spatial interpolants modulo theories, Splinter
can infer complex invariants over general recursive predicates, e.g., of the
form all elements in a linked list are even or a binary tree is sorted.
Furthermore, we treat interpolation as a black box, which gives us the freedom
to encode data manipulation in any suitable theory for a given program (e.g.,
bit vectors, arrays, or linear arithmetic), so that our technique immediately
benefits from any future advances in SMT solving and interpolation.Comment: Short version published in ESOP 201
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