Bibliographic record
Abstract
Successful software must be both useful and innovative. Techniques for Requirements Engineering (RE) have mainly focused on utility, with a prominent body of work using goal modeling and analysis to ensure that systems meet user goals. However, these techniques are not designed to foster creativity, meaning that resulting systems may be functionally useful but not sufficiently innovative. Further work has focused on applying creativity techniques for RE through workshops. However, the free-form representation of creative workshop outputs (text and informal diagrams), although flexible, is not grounded in user goals, or able to take advantage of goal model analysis, e.g., trade-off analysis. Furthermore, successfully conducting a creative RE workshop requires much experience and soft-skills, as well as a significant economic commitment. In this work, we summarize initial progress aiming to combine goal modeling and creativity techniques for enhanced RE. We focus on methods and tools for introducing creative ideas to goal modeling, and grounding creative outputs in goal-oriented models. Our focus on tooling and methods help to alleviate the need for expert-lead, costly workshops. We outline and illustrate proposed methods.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".