Assurance via model transformations and their hierarchical refinement
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
Assurance is a demonstration that a complex system (such as a car or a communication network) possesses an importantproperty, such as safety or security, with a high level of confidence. In contrast to currently dominant approaches to building assurance cases, which are focused on goal structuring and/or logical inference, we propose considering assurance as a model transformation (MT) enterprise: saying that a system possesses an assured property amounts to saying that a particular assurance view of the system comprising the assurance data, satisfies acceptance criteria posed as assurance constraints. While the MT realizing this view is very complex, we show that it can be decomposed into elementary MTs via a hierarchy of refinement steps. The transformations at the bottom level are ordinary MTs that can be executed for data specifying the system, thus providing the assurance data to be checked against the assurance constraints. In this way, assurance amounts to traversing the hierarchy from the top to the bottom and assuring the correctness of each MT in the path. Our approach has a precise mathematical foundation (rooted in process algebra and category theory) --- a necessity if we are to model precisely and then analyze our assurance cases. We discuss the practical applicability of the approach, and argue that it has several advantages over existing approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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