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Author:   Xavier Rival
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   1st ed. 2016
Volume:   9837
Dimensions:   Width: 15.50cm , Height: 2.70cm , Length: 23.50cm
Weight:   0.795kg
ISBN:  

9783662534120

ISBN 10:   3662534126
Pages:   511
Publication Date:   31 August 2016
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Quantitative Monitor Automata.- The Julia Static Analyzer for Java.- Automated Verification of Linearization Policies.- Structure-Sensitive Points-To Analysis for C and C.- Bounded Abstract Interpretation.- Completeness in Approximate Transduction.- Relational Verification through Horn clause transformation.- Securing A Compiler Transformation.- Exploiting sparsity in difference-bound matrices.- Flow- and Context-Sensitive Points-to Analysis using Generalized Points-to Graphs.- Learning a Variable-Clustering Strategy for Octagon From Labeled Data Generated by a Static Analysis.- Static Analysis by Abstract Interpretation of the Functional Correctness of Matrix Manipulating Program.- Generalized Homogeneous Polynomials for Efficient Template-Based Nonlinear Invariant.- On the linear ranking problem for simple floating-point loops.- Alive-FP: Automated Verification of Floating Point Based Peephole Optimizations in LLVM.- A Parametric Abstract Domain for Lattice-Valued Regular Expressions.- Cell morphing: from array programs to array-free Horn clauses.- Loopy: Programmable and Formally Verified Loop Transformations.- Abstract Interpretation of Supermodular Games.- Validating Numerical Semidefinite Programming Solvers for Polynomial Invariants.- Enforcing Termination of Interprocedural Analysis.- From Array Domains to Abstract Interpretation under Store-Buffer-Based Memory Models.- Making k-Object-Sensitive Pointer Analysis More Precise with Still k-Limiting.  

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