Static Analysis (eBook, PDF)

29th International Symposium, SAS 2022, Auckland, New Zealand, December 5-7, 2022, Proceedings
Redaktion: Singh, Gagandeep; Urban, Caterina

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 29th International Symposium on Static Analysis, SAS 2022, held in Auckland, New Zealand, in December 2022.

The 18 full papers included in this book were carefully reviewed and selected from 43 submissions. Static analysis is widely recognized as a fundamental tool for program verification, bug detection, compiler optimization, program understanding, and software maintenance. The papers deal with theoretical, practical and application advances in the area.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 475
• Erscheinungstermin: 1. Dezember 2022
• Englisch
• ISBN-13: 9783031223082
• Artikelnr.: 66781481

Inhaltsangabe

Invited Talks.- Specification-Guided Reinforcement Learning.- Towards Efficient Reasoning of Quantum Programs.- Regular Papers.- Solving Invariant Generation for Unsolvable Loops.- Principles of Staged Static+Dynamic Partial Analysis.- SecWasm: Information Flow Control for WebAssembly.- Lifting Numeric Relational Domains to Algebraic Data Types.- Automated Synthesis of Asynchronizations.- Case Study on Veri cation-Witness Validators: Where We Are and Where We Go.- Deciding program properties via complete abstractions on boundeddomains.- Invariant Inference With Provable Complexity From the Monotone Theory.- Efficient Modular SMT-Based Model Checking of Pointer Programs.- Property-driven code obfuscations - Reinterpreting Jones-optimality in Abstract Interpretation.- Bootstrapping Library-Based Synthesis.- Boosting Robustness Veri cation of Semantic Feature Neighborhoods.- Fast and incremental computation of weak control closure.- Local Completeness Logic on Kleene Algebra with Tests.- Semantic Foundations for Cost Analysis of Pipeline-Optimized Programs.- Parameterized Recursive Re nement Types for Automated Program Verification.- Adversarial Logic.- CLEVEREST: Accelerating CEGAR-based Neural Network Verification via Adversarial Attacks.

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