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Static Analysis: 28th International Symposium, SAS 2021, Chicago, IL, USA, October 1719, 2021, Proceedings 1st ed. 2021 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 479 pages, height x width: 235x155 mm, weight: 753 g, 97 Illustrations, color; 69 Illustrations, black and white; XVI, 479 p. 166 illus., 97 illus. in color., 1 Paperback / softback
  • Sērija : Programming and Software Engineering 12913
  • Izdošanas datums: 14-Oct-2021
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030888053
  • ISBN-13: 9783030888053
  • Mīkstie vāki
  • Cena: 73,68 €*
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  • Formāts: Paperback / softback, 479 pages, height x width: 235x155 mm, weight: 753 g, 97 Illustrations, color; 69 Illustrations, black and white; XVI, 479 p. 166 illus., 97 illus. in color., 1 Paperback / softback
  • Sērija : Programming and Software Engineering 12913
  • Izdošanas datums: 14-Oct-2021
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030888053
  • ISBN-13: 9783030888053
This book constitutes the refereed proceedings of the 28th International Symposium on Static Analysis, SAS 2021, held in Chicago, IL, USA, in October 2021.

The 18 regular and 4 short papers, carefully reviewed and selected from 48 submissions, are presented in this book  together with 1-page summaries of the three invited talks. The papers cover topics such as static program analysis, abstract domain, abstract interpretation, automated deduction, debugging techniques, deductive methods, model checking, data science, program optimizations and transformations, program synthesis, program verification, and security analysis.


Fast and Efficient Bit-Level Precision Tuning.- Backward Symbolic
Execution with Loop Folding.- Accelerating Program Analyses in Datalog by
Merging Library Facts.- Abstract Interpretation.- Verified Functional
Programming of an Abstract Interpreter.- Disjunctive Interval Analysis.-
Static analysis of ReLU neural networks with tropical polyhedral.- Exploiting
Verified Neural Networks via Floating Point Numerical Error.-Verifying
Low-dimensional Input Neural Networks via Input Quantization.- Data
Abstraction: A General Framework to Handle Program.- Verification of Data
Structures.- Toward Neural-Network-Guided Program Synthesis and
Verification.- Selective Context-Sensitivity for k-CFA with
CFL-Reachability.- Selectively-Amortized Resource Bounding.- Reduced Products
of Abstract Domains for Fairness Certification of Neural Networks.- A
Multi-Language Static Analysis of Python Programs with Native C Extensions.-
Automated Verification of the Parallel BellmanFord Algorithm.- Improving
Thread-Modular Abstract Interpretation.- Thread-modular Analysis of
Release-Acquire Concurrency.- Symbolic Automatic Relations and Their
Applications to SMT and CHC Solving.- Compositional Verification of Smart
Contracts Through Communication Abstraction.- Automatic Synthesis of
Data-Flow Analyzer.