Rigorous Safety-Critical Cyber-Physical Systems Development using Formal Methods
Bibliographic record
Abstract
Today, we are surrounded by digital technologies and highly complex systems, where safety-critical cyber-physical systems have taken central place in our lives and in various industrial sectors to improve human lives and boost economies by solving key issues in a variety of domains, including defense, transportation, space, healthcare and biomedical, agriculture, manufacturing, smart grids and energy, and everyday living. With great utility, however, safety-critical cyber-physical systems brought very important issues in their development, particularly in system modelling, security and privacy, heterogeneity, composition, and certification, which could jeopardize our well-being as well as the development and reliability of cyber-physical systems. Our increased reliance on safety-critical cyber-physical systems also prompted us to consider the ethics of these systems and how future technologies might limit risks related to failure, safety, privacy, responsibility, liability, and other issues.We argue that addressing some of these essential questions requires combining formal approaches with key domains like domain knowledge engineering, system modelling, and certification for rigorous formal engineering of safety-critical cyber-physical systems. Formal methods play a key role to model such complex systems correctly.Domain knowledge engineering is useful for specifying essential elements that can be used to develop domain models and establishing relationships between system models. System modelling enables the development of generic frameworks, modelling and designing patterns, formal theories and proofs, and implementation for addressing design challenges. Finally, certification methods aid in the certification of complex safety-critical cyber-physical systems and their components.This manuscript synthesises our research efforts on the development and investigation of methods for dealing with formal engineering processes such as modelling, refinement and simulation, domain knowledge engineering, design automation, heterogeneity, composition, safety, and certification issues for safety-critical cyber-physical systems. Our first contribution focuses on domain knowledge engineering for dealing with their various core concepts. The next contribution focuses on system modelling, covering various approaches such as automatic refinement, modelling and designing framework, patterns and theories, reflexive meta-modelling, environment modelling and automatic code generation. The last contribution focuses on certification and the development of assurance cases. Finally, we deploy these approaches to the design of safety-critical cyber-physical systems from various domains. We conclude by describing the perspectives of our research, which include two main directions: (i) perspectives on theories, models, patterns, and tools, and (ii) perspectives on safety-critical cyber-physical systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".