Towards a compliance meta-model for system requirements in contractual projects
Bibliographic record
Abstract
In contractual systems engineering projects, the developing organization is often required to demonstrate compliance of the system's requirements against a myriad of engineering standards and government regulations. In order to satisfy this goal, the project requirements imposed by standards and regulations through the contract need to be traceable to/fro appropriate project artefacts (such as the contract, various system and sub-system requirements specifications, standards, regulatory documents, etc.). However, these artefacts form a complex interrelationship network, leading to significant challenges in demonstrating requirements compliance. Current practices dealing with such compliance are ad hoc and arduous. In this paper, we identify key artefacts, relationships and challenges that we are currently discovering from a case study on a large-scale, contractual, requirements compliance project. These findings can be a basis for creating a meta-model for requirements compliance in a systems engineering project. This paper describes the on-going case study, the emerging findings, and their implication.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".