A Quality Assurance Model for Airborne Safety-Critical Software
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
The tragic nature of safety-critical software failure’s consequences makes high quality and extreme reliability requirements in such types of software of paramount importance. Far too many accidents have been caused by software failure error or where such failure/error was part of the problem. Safety-critical software is widely applied in diverse areas, ranging from medical equipment to airborne systems. Currently, the trend in the use of safety-critical software in the aerospace industry is mostly concentrated on avionic systems. While standards for certification and development of safety-critical software have been developed by authorities and the industry, very little research has been done to address safety-critical software quality. In this paper, we study safety-critical software embedded in airborne systems. We propose a lifecycle specially modeled for the development of safety-critical software in compliance with the DO-178B standard and a software quality assurance (SQA) model based on a set of four acceptance criteria that builds quality into safety-critical software throughout its development.
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 | 0.000 |
| 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