Do viewpoints lead to better conceptual models? An exploratory case study
Bibliographic record
Abstract
The use of viewpoints has long been proposed as a technique to structure evolving requirements models. In theory, viewpoints should provide better stakeholder traceability, and the ability to discover important requirements by comparing viewpoints. However, this theory has never been tested empirically. This paper reports on an exploratory case study of a key hypothesis of the viewpoints theory, namely that by creating separate viewpoint models to represent different stakeholder contributions, and explicitly merging them, important hidden requirements can be discovered. The case study compared two modelling teams using the i* notation to capture requirements for new Web-based counselling services for a large charitable organisation. One team used viewpoints; the other did not. The conclusions include that viewpoint merging improves the understanding of the problem domain, but is very time consuming. The process of merging was more important than the merged product. The study also indicates a need for better model management tools, as both teams encountered difficulty in managing large, evolving models.
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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 | 0.032 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".