A Structured Method for Requirements Analysis with Application to CFR-14 Part-21 Subpart-G
Bibliographic record
Abstract
Standards are the bottleneck of any aviation industrial action.Thus, meeting standards is not an option but an obligation throughout a given product life cycle.The implementation of the standards and the evaluation process are complicated and need considerable resources.Simultaneously, Extra costs and concerns may be imposed on the aviation industry due to improper requirements.Thus, the quality of standards should be comprehensively assessed to obtain a satisfactory final result.System engineering introduces requirement analysis to solve this problem.As a significant contribution, this paper provides a method of requirement analysis based on the structured strategy.The criteria were defined practically.Diagram, process, sub-process, and techniques including RTM and WBS were either developed or deployed to investigate the conformance of requirements with the criteria.Applying this method, any sort of requirement is evaluated accurately.Plus, it shows a specific and root cause of the problems if the requirement is unacceptable.Furthermore, a novel way to measure system affordability is proposed through the application of DFMEA.Lastly, the model is applied on CFR-14 Part-21 Subpart-G (FAA production organization requirements) to show the need for systematic improvement of aviation standards' requirements of production organization.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".