Beyond Boxes and Lines: Creating and Empirically Evaluating Alternative Visualizations for Requirements Conceptual Models
Bibliographic record
Abstract
[Background]: Conceptual modeling languages have been widely studied in requirements engineering as tools for capturing, representing and reasoning about domain problems. One of these languages, goal models, has been proposed for representing the structure of stakeholder intentions. Like most other conceptual modeling languages, goal models are visualized using box-and-line diagrammatic notations. But is this box-and-line approach the best way for visualizing goals and relationships thereof? [Aims]: Through a series of experimental studies we have recently endeavored to find out. In this presentation, we describe features of our alternative visualization proposals and present experiences gained from our attempts to empirically evaluate them. [Results]: Central to what we learned is the usefulness of distinguishing between language visualization and intended language semantics and of measuring the degree by which the former serves correct recognition of the latter. [Conclusions]: Our experience from these studies could be useful for those interested in experimentally-driven conceptual modeling language design.
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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 | 0.089 | 0.432 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".