Impediments to Regulatory Compliance of Requirements in Contractual Systems Engineering Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it