Building a software requirements specification and design for an avionics system
Bibliographic record
Abstract
As with many of the products and systems that pervade us, aircraft rely more and more on software for controlling the behaviour of their systems. In consequence, the field has seen increased work around more up-to-date, effective software engineering technologies for aiding avionics software providers in reducing software and development complexities and supporting them in their certification endeavours. However, there is a lack in the literature of reusable, comprehensive references about avionics software developments in conformance with DO-178C. Moreover, there is a need for a benchmark specification to support the evaluation of proposed engineering approaches in the field. This paper presents a software development case study of an avionics control software for a landing gear system. All the documentation for the software's requirements specification and design has been developed to conform with the DO-178C guideline and the applicable DO-331 and DO-332 supplements for model-based and object-oriented development, respectively. A requirements specification and design methodology is proposed and followed for the construction of the software in the case study. Furthermore, the paper discusses the observations, and challenges and issues experienced throughout the process.
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.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".