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 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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".