Modeling and Reasoning with Changing Intentions: An Experiment
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.
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".