Formal Methods and Software Engineering : 24th International Conference on Formal Engineering Methods, ICFEM 2023, Brisbane, QLD, Australia, November 21-24, 2023, Proceedings / / Yi Li and Sofiène Tahar, editors |
Edizione | [First edition.] |
Pubbl/distr/stampa | Singapore : , : Springer, , [2024] |
Descrizione fisica | 1 online resource (319 pages) |
Disciplina | 004.0151 |
Collana | Lecture Notes in Computer Science Series |
Soggetto topico |
Formal methods (Computer science)
Software engineering |
ISBN | 981-9975-84-0 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Invited Talk: Verifying Compiler Optimisations -- Regular Papers: An Idealist’s Approach for Smart Contract Correctness -- Active Inference of EFSMs Without Reset -- Learning Mealy Machines with Local Timers -- Compositional Vulnerability Detection with Insecurity Separation Logic -- Dynamic Extrapolation in Extended Timed Automata -- Formalizing Robustness against Character-level Perturbations for Neural Network Language Models -- Trace models of concurrent valuation algebras -- Branch and Bound for Sigmoid-like Neural Network Verification -- Certifying Sequential Consistency of Machine Learning Accelerators -- Guided Integration of Formal Verification in Assurance Cases -- Validation-Driven Development -- Incremental Property Directed Reachability -- Proving Local Invariants in ASTDs -- Doctoral Symposium Papers: Formal Verification of the Burn-to-Claim Blockchain Interoperable Protocol -- Early and systematic validation of formal models -- Verifying Neural Networks by Approximating Convex Hulls -- Eager to Stop: Efficient Falsification of Deep Neural Networks -- A Runtime Verification Framework For Cyber-physical Systems Based On Data Analytics And LTL Formula Learning -- Unified Verification of Neural Networks’ Robustness and Privacy in Computer Vision -- IoT Software Vulnerability Detection Techniques through Large Language Model -- Vulnerability Detection via Typestate-Guided Code Representation Learning. |
Record Nr. | UNISA-996565870603316 |
Singapore : , : Springer, , [2024] | ||
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Lo trovi qui: Univ. di Salerno | ||
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Formal Methods and Software Engineering : 24th International Conference on Formal Engineering Methods, ICFEM 2023, Brisbane, QLD, Australia, November 21-24, 2023, Proceedings / / Yi Li and Sofiène Tahar, editors |
Edizione | [First edition.] |
Pubbl/distr/stampa | Singapore : , : Springer, , [2024] |
Descrizione fisica | 1 online resource (319 pages) |
Disciplina | 004.0151 |
Collana | Lecture Notes in Computer Science Series |
Soggetto topico |
Formal methods (Computer science)
Software engineering |
ISBN | 981-9975-84-0 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Invited Talk: Verifying Compiler Optimisations -- Regular Papers: An Idealist’s Approach for Smart Contract Correctness -- Active Inference of EFSMs Without Reset -- Learning Mealy Machines with Local Timers -- Compositional Vulnerability Detection with Insecurity Separation Logic -- Dynamic Extrapolation in Extended Timed Automata -- Formalizing Robustness against Character-level Perturbations for Neural Network Language Models -- Trace models of concurrent valuation algebras -- Branch and Bound for Sigmoid-like Neural Network Verification -- Certifying Sequential Consistency of Machine Learning Accelerators -- Guided Integration of Formal Verification in Assurance Cases -- Validation-Driven Development -- Incremental Property Directed Reachability -- Proving Local Invariants in ASTDs -- Doctoral Symposium Papers: Formal Verification of the Burn-to-Claim Blockchain Interoperable Protocol -- Early and systematic validation of formal models -- Verifying Neural Networks by Approximating Convex Hulls -- Eager to Stop: Efficient Falsification of Deep Neural Networks -- A Runtime Verification Framework For Cyber-physical Systems Based On Data Analytics And LTL Formula Learning -- Unified Verification of Neural Networks’ Robustness and Privacy in Computer Vision -- IoT Software Vulnerability Detection Techniques through Large Language Model -- Vulnerability Detection via Typestate-Guided Code Representation Learning. |
Record Nr. | UNINA-9910760278703321 |
Singapore : , : Springer, , [2024] | ||
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Lo trovi qui: Univ. Federico II | ||
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