A Domain Specific Language for the ARINC 653 Specification
Bibliographic record
Abstract
With the introduction of the integrated modular avionics (IMA), recent trends in avionics are to integrate dif-ferent software applications on the same hardware platform. In this context, the underlying platform embodied by a real-time operating system (RTOS) must be designed in compliance with the ARIN C 653 specification. ARIN C 653 defines an application executive (APEX) interface between the RTOS and avionics applications within IMA architecture. It specifies requirements of an environment that provides partitioning, i.e. separation of applications to ensure fault containment and ease of verification. Designing an RTOS that complies with ARIN C 653 is costly and requires significant efforts. In this paper, we introduce a domain-specific language (DSL) that supports the specification of an ARINC653-compliant RTOS. In particular, we consider ARINC 653 as a set of generic and high-level requirements, and we use model-driven technologies to specify these requirements in the form of a metamodel. The ARINC metamodel aims at supporting and reducing the cost of certification by reusing the metamodel across multiple RTOS development projects. Other benefits of the ARIN C metamodel include generating data required for certification such as ARIN C configuration tables and test data.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".