Safety Properties of Hybrid System Product Lines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hybrid systems are an important class of Cyber-physical systems. Hybrid systems are characterized by a combination of discrete and continuous dynamics. Over the last two decades, research has focused on formal techniques and tools for proving properties of hybrid systems, these techniques have matured to the point where they are ready for industrial application. An advantage of the existing formal techniques is their ability to prove safety properties over a range of model parameters and thus allow for results to be generalized to an entire product line. However, a critical barrier to industrial adoption of formal techniques is their integration with widely adopted industrial standards. This paper identifies “parameterized hybrid systems” as an extension of the existing notion of a hybrid system and provides a formal definition based on foundational theory from the domain of software product line engineering. Using this definition, an engineering procedure is proposed to aid in proving properties over many choices of system parameters for a product line. The proposed engineering procedure is discussed in the context of several widely adopted industrial standards (ISO 26262, DO-178C, and EN 50128) which contain gaps regarding the use of formal methods for proving safety of parameterized systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it