Supporting Consistency in the Heterogeneous Design of Safety-Critical Software
Bibliographic record
Abstract
Safety-critical software are highly heterogeneous, possessing very different characteristics. These characteristics are described using diverse modelling mechanisms (e.g., MathWorks Simulink and Stateflow, UML). The different resulting models may facilitate understanding and communication, but hinder verification and certification. This is in part due to the fact that design models have to be kept consistent, specially in cases where overlaps exist. Moreover, where overlapping exists, mappings between overlapping elements are required. In particular, the regulated nature of these systems, along with the size and complexity of their design models requires well-defined guidelines for ensuring model consistency. This paper presents a model-driven approach for verifying consistency between UML, Simulink and Stateflow design models, and for recording mappings between overlapping elements in them. The approach is intended to be part of the design standards and process of avionics companies to help them comply with DO-178C. An avionics industrial case study is used to motivate the work and demonstrate the proposed approach.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".