Engineering Stakeholders' Viewpoint-concerns for Architecting a Modern Enterprise
Bibliographic record
Abstract
Stakeholders in an enterprise have diverse concerns from a variety of viewpoints. Designers of the enterprise and its systems elicit, analyze, and engineer stakeholders' viewpoint concerns. Most, if not all, of the stakeholders' concerns, are written in natural language. Ambiguity is omnipresent when it comes to describing viewpoint concerns in natural language. This ambiguity may lead to sub-par requirements for systems design and documenting enterprise blueprints. Many requirements elicitation methods and enterprise architecture frameworks emerged in past decades and gained popularity. None of these linked the viewpoint concerns to natural language lexical and semantic constructs. In this dissertation, we describe English language interrogative investigations and their semantic and lexical relationships to formulate better viewpoint concerns for stakeholders. We included two publications, primarily for waterfall software development lifecycles and monolith applications, one on requirements engineering and the other on enterprise architecture. In the past decade, widespread adoption of agile project management, independent delivery with microservices, and automated deployment with DevOps has tremendously sped up systems development. Microservices are provisioned and accessed via application programming interfaces (APIs). APIs carry business value and have created an API economy for enterprises. The link between stakeholders’ concerns and microservices/APIs is not well captured nor adequately defined. We included a third publication in this dissertation that addresses these issues.Based on the publications included in the dissertation, we created a tool that uses a natural language generation system to auto formulate stakeholder viewpoint concerns for a system.
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.
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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".