Modeling and Reasoning with Changing Intentions: An 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
Existing modeling approaches in requirements engineering assume that stakeholder goals are static: once set, they remain the same throughout the lifecycle of the project. Of course, such goals, like anything else, may change over time. In earlier work, we introduced Evolving Intentions: an approach that allows stakeholders to specify how evaluations of goal model elements change over time. Simulation over Evolving Intentions enables stakeholders to ask a variety of 'what if' questions, and evaluate possible evolutions of a goal model. GrowingLeaf is a web-based tool that implements both the modeling and analysis components of this approach. In this paper, we investigate the effectiveness and usability of Evolving Intentions, Simulation over Evolving Intentions, and GrowingLeaf. We report on a between-subjects experiment we conducted with fifteen graduate students familiar with requirements engineering. Using qualitative, quantitative, and timing data, we show that Evolving Intentions were intuitive, that Simulation over Evolving Intentions increased the subjects' understanding and produced meaningful results, and that GrowingLeaf was found to be effective and usable.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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