Safety Assurance under Uncertainties
From Software to Cyber-Physical/Machine Learning Systems
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Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 13 May 2025
- ISBN 9780367554019
- Binding Hardback
- No. of pages366 pages
- Size 234x156 mm
- Language English
- Illustrations 117 Illustrations, black & white; 8 Illustrations, color; 68 Halftones, black & white; 4 Halftones, color; 49 Line drawings, black & white; 4 Line drawings, color 700
Categories
Short description:
Modern software systems operate under an unprecedented degree of uncertainties, making them hard to specify, model, test, analyze, and verify. Safety assurance of such systems requires efforts that unite different disciplines such as formal methods, software science, software engineering, control theory, machine learning.
MoreLong description:
Safety assurance of software systems has never been as imminent a problem as it is today. Practitioners and researchers who work on the problem face a challenge unique to modern software systems: uncertainties. For one, the cyber-physical nature of modern software systems as exemplified by automated driving systems mandates environmental uncertainties to be addressed and the resulting hazards to be mitigated. Besides, the abundance of statistical machine-learning components massive numerical computing units for statistical reasoning such as deep neural networks make systems hard to explain, understand, analyze or verify.
The book is the first to provide a comprehensive overview of such united and interdisciplinary efforts. Driven by automated driving systems as a leading example, the book describes diverse techniques to specify, model, test, analyze, and verify modern software systems. Coming out of a collaboration between industry and basic academic research, the book covers both practical analysis techniques (readily applicable to existing systems) and more long-range design techniques (that call for new designs but bring a greater degree of assurance).
The book provides high-level intuitions and use-cases of each technique, rather than technical details, with plenty of pointers for interested readers.
MoreTable of Contents:
Preface. Optimisation-Based Falsification. Monitoring Temporal Specifications. Formal Specification of Temporal Properties. Testing for Machine Learning-Based Systems. Safety Standards and Safety Assurance Framework for ADS. Uncertainty-wise Testing. Decision Making for Automated Driving. Formal Modelling. Theorem Proving at Work. Search-Based Analysis and Engineering. Fault Localisation and Understanding. Index.
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