Certification Considerations of Software-Defined Radio Using Model-Based Development and Automated Testing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it