6 research outputs found

    Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference

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    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

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    PETITION FOR ORIGINAL WRIT OF MANDAMUS DIRECTED TO THE HONORABLE DAVID L. MOWER DISTRICT JUDGE OF SEVIER COUNTY, STATE OF UTA

    Generating Non-Linear Interpolants by Semidefinite Programming

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    Interpolation-based techniques have been widely and successfully applied in the verification of hardware and software, e.g., in bounded-model check- ing, CEGAR, SMT, etc., whose hardest part is how to synthesize interpolants. Various work for discovering interpolants for propositional logic, quantifier-free fragments of first-order theories and their combinations have been proposed. However, little work focuses on discovering polynomial interpolants in the literature. In this paper, we provide an approach for constructing non-linear interpolants based on semidefinite programming, and show how to apply such results to the verification of programs by examples.Comment: 22 pages, 4 figure

    Tools and Algorithms for the Construction and Analysis of Systems

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    Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference

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    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
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