Evaluation of Goal Models in Reuse Hierarchies with Delayed Decisions
Bibliographic record
Abstract
Trade-off analysis through goal model evaluation has been a valuable tool for requirements elicitation and analysis. This is also true in the context of reuse. When goal models are used to describe reusable artifacts and to represent the impacts of reusable artifacts on high-level goals and qualities, they can guide the selection of reusable artifacts to build reuse hierarchies. In previous work, we introduced the use of relative contribution values for reusable goal models, while considering constraints imposed by other modeling notations. In this paper, we expand the result of goal model evaluation from the typical single satisfaction value to a range of values that are still possible based on the current task selections. In the context of reuse hierarchies, we call the remaining task selections delayed decisions because they are postponed to a higher level in the reuse hierarchy when more is known about the system under development. The extended algorithm takes into account the delayed decisions and evaluates the best and worst possible results that can be obtained with the task selections that have been made in the entire reuse hierarchy. The distinct levels in the reuse hierarchy are leveraged to manage the computational complexity of this reuse hierarchy-wide evaluation. A proof-of-concept implementation of the novel evaluation algorithm is presented in the concern-oriented software design modeling tool TouchCORE.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".