Certification Considerations of Software-Defined Radio Using Model-Based Development and Automated Testing
Bibliographic record
Abstract
In this article, we present a set of methods to accelerate the development process and the verification process of certifiable Software-Defined Radio (SDR) applications, including both Model-Based Development (MBD) methodology and automated testing (unit and integration) technology. We demonstrate the feasibility with a case study, where an Instrument Landing System (ILS) in the domain of SDR avionics applications is presented, in which part of the code (for signal processing) is automatically generated from models and the remaining (for integration) code is not. The proposed methods strive to accelerate the compliance with the DO-178C standard’s dynamic testing requirements. We consider the integration of the proposed methods to a system’s certification processes in the context of the case study. The main contribution of this paper consists of integrating the MBD and the automated testing methods, and mapping them to the certification processes of SDR by respecting the set of instructions specified in the standard DO-178C.
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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 | 0.006 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".