Verification and testing of safety-critical airborne systems: A model-based methodology
Bibliographic record
Abstract
In this paper, we address the issues of safety-critical software verification and testing that are key requirements for achieving DO-178C and DO- 331 regulatory compliance for airborne systems. Formal verification and testing are considered two different activities within airborne standards and they belong to two different levels in the avionics software development cycle. The objective is to integrate model-based verification and model-based testing within a single framework and to capture the benefits of their cross-fertilization. This is achieved by proposing a new methodology for the verification and testing of parallel communicating agents based on formal models. In this work, properties are extracted from requirements and formally verified at the design level, while the verified properties are propagated to the implementation level and checked via testing. The contributions of this paper are a methodology that integrates verification and testing, formal verification of some safety critical software properties, and a testing method for Modified Condition/Decision Coverage (MC/DC). The results of formal verification and testing can be used as evidence for avionics software certification.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".