Impediments to Regulatory Compliance of Requirements in Contractual Systems Engineering Projects
Bibliographic record
Abstract
Large-scale contractual systems engineering projects often need to comply with myriad government regulations and standards as part of contractual obligations. A key activity in the requirements engineering (RE) process for such a project is to demonstrate that all relevant requirements have been elicited from the regulatory documents and have been traced to the contract as well as to the target system components. That is, the requirements have met regulatory compliance. However, there are impediments to achieving this level of compliance due to such complexity factors as voluminous contract, large number of regulatory documents, and multiple domains of the system. Little empirical research has been conducted in the scientific community on identifying these impediments. Knowing these impediments is a driver for change in the solutions domain (i.e., creating improved or new methods, tools, processes, etc.) to deal with such impediments. Through a case study of an industrial RE project, we have identified a number of key impediments to achieving regulatory compliance in a large-scale, complex, systems engineering project. This project is an upgrade of a rail infrastructure system. The key contribution of the article is a number of hitherto uncovered impediments described in qualitative and quantitative terms. The article also describes an artefact model, depicting key artefacts and relationships involved in such a compliance project. This model was created from data gathered and observations made in this compliance project. In addition, the article describes emergent metrics on regulatory compliance of requirements that can possibly be used for estimating the effort needed to achieve regulatory compliance of system requirements.
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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 | 0.099 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".