Assessing the Intuitiveness of Qualitative Contribution Relationships in Goal Models: An Exploratory Experiment
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
[Background]: Developing conceptual models is an integral part of the requirements engineering (RE) process. Goal models are requirements engineering conceptual models that allow diagrammatic representation of stakeholder intentions and how they affect each other. A specific goal modeling language construct, the contribution of goal satisfaction of one goal to another, plays a central role in supporting decision problem exploration within goal models. We report on an experiment whose aim was to measure the user perception of the meaning of the aforementioned modeling construct. A set of contributions under different scenarios were given to experimental participants who were asked what they thought the effect of the contribution was. We found that participants are not always in agreement either within themselves or with the designers' intentions on the meaning of the language. The results call for possible adaptations to the way goal modeling languages are used.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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