On the Exploration of Model-Based Support for DO-178C-Compliant Avionics Software Development and Certification
Bibliographic record
Abstract
Vital functions of avionics systems nowadays depend highly on software. Engineering such safety-critical software is not straightforward as authorities impose stringent regulation like DO-178. Besides, the more functions the software has to provide, the more complex it becomes. Thus, effective engineering methods are required. In this context, DO-178C now considers contemporary software development techniques like Model-Driven Engineering. In particular Model-Driven Engineering has gained interest as a cost-and time-effective alternative reducing software development complexities by enabling reasoning at the model level. In this paper we present a review of a set of model-based approaches to assess their support for software development and certification under DO-178C. We built a framework to characterize these approaches according with several criteria, specially coverage of DO-178C's required information for compliance. We analyze the approaches using this framework and highlight their commonalities, differences, strengths and weaknesses. Additionally, we identify open issues on which research may focus.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".