Titlebook: Static Analysis; 26th International S Bor-Yuh Evan Chang Conference proceedings 2019 Springer Nature Switzerland AG 2019 logics and meaning

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Abstract Interpretation of Indexed Grammarscognized by indexed grammars are called indexed languages and they correspond to the languages recognized by nested stack automata. For example indexed grammars can recognize the language . which is not context-free, but they cannot recognize . which is context-sensitive. Indexed grammars identify a

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Language Inclusion Algorithms as Complete Abstract Interpretationsbstraction of ., obtained by successively overapproximating the Kleene iterates of its least fixpoint characterization, is included in .. We show that a language inclusion problem is decidable whenever this overapproximating abstraction satisfies a completeness condition (i.e. its loss of precision

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An Efficient Parametric Linear Programming Solver and Application to Polyhedral Projection efficient than the classic Fourier-Motzkin elimination method..In prior work, PLP was done in arbitrary precision rational arithmetic. In this paper, we present an approach where most of the computation is performed in floating-point arithmetic, then exact rational results are reconstructed..We als

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Analysis of Software Patches Using Numerical Abstract Interpretationand prove that both programs compute the same outputs when run on the same inputs. Our method is based on abstract interpretation, and parametric in the choice of an abstract domain. We focus on numeric properties only. Our method is able to deal with unbounded executions of infinite-state programs,

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Verifying Numerical Programs via Iterative Abstract Testingn target property, and being hardly able to generate counter-examples when the property does not hold. In this paper, we propose iterative abstract testing, to create a property-oriented verification approach based on abstract interpretation. Abstract testing employs forward abstract executions (i.e

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Robustness Verification of Support Vector Machinesr classification and regression tasks. Following a recent stream of works on formal robustness verification of (deep) neural networks, our approach relies on a sound abstract version of a given SVM classifier to be used for checking its robustness. This methodology is parametric on a given numerical

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Conference proceedings 2019019.. The 20 regular papers presented in this book were carefully reviewed and selected from 50 submissions. . The papers are grouped in topical sections on pointers and dataflow; languages and decidability; numerical; trends: assuring machine learning; synthesis and security; and temporal propertie