Verification, Model Checking, and Abstract Interpretation (eBook, PDF)

25th International Conference, VMCAI 2024, London, United Kingdom, January 15-16, 2024, Proceedings, Part I
Redaktion: Dimitrova, Rayna; Wolff, Sebastian; Lahav, Ori

• Format: PDF

Produktbeschreibung

The two-volume set LNCS 14499 and 14500 constitutes the proceedings of the 25th International Conference on Verification, Model Checking, and Abstract Interpretation, VMCAI 2024, which took place in London, Ontario, Canada, in January 2024.

The 30 full papers presented in the proceedings were carefully reviewed and selected from 74 submissions. They were organized in topical sections as follows:

Part I: Abstract interpretation; infinite-state systems; model checking and synthesis; SAT, SMT, and automated reasoning;

Part II: Concurrency; neural networks; probabilistic and quantum programs; program and system verification; runtime verification; security and privacy.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 352
• Erscheinungstermin: 29. Dezember 2023
• Englisch
• ISBN-13: 9783031505249
• Artikelnr.: 69718656

Inhaltsangabe

Abstract Interpretation.- Formal Runtime Error Detection During Development in the Automotive Industry.- Abstract Interpretation-Based Feature Importance for Support Vector Machines.- Generation of Violation Witnesses by Under-Approximating Abstract Interpretation.- Correctness Witness Validation by Abstract Interpretation.- Infinite-State Systems.- Project and Conquer: Fast Quantifier Elimination for Checking Petri Net Reachability.- Parameterized Verification of Disjunctive Timed Networks.- Resilience and Home-Space for WSTS.- Model Checking and Synthesis.- Generic Model Checking for Modal Fixpoint Logics in COOL-MC.- Model-Guided Synthesis for LTL over Finite Traces.- Solving Two-Player Games under Progress Assumptions.- AT, SMT, and Automated Reasoning Interpolation and Quantifiers in Ortholattices.- Function Synthesis for Maximizing Model Counting.- Boosting Constrained Horn Solving by Unsat Core Learning.- On the Verification of a Subgraph Construction Algorithm.- Efficient Local Search for Nonlinear Real Arithmetic.