On eliciting contribution measures in goal models
Bibliographic record
Abstract
Goal models have been found to be useful for supporting the decision making process in the early requirements phase. Through measuring contribution degrees of low-level decisions to the fulfilment of high-level quality goals and combining them with priority statements, it is possible to compare alternative solutions of the requirements problem against each other. But where do contribution measures come from and what is the right way to combine them in order to do such analysis? In this paper we describe how full application of the Analytic Hierarchy Process (AHP) can be used to quantitatively assess contribution relationships in goal models based on stakeholder input and how we can reason about the result in order to make informed decisions. An exploratory experiment shows that the proposed procedure is feasible and offers evidence that the resulting goal model is useful for guiding a decision. It also shows that situation-specific characteristics of the requirements problem at hand may influence stakeholder input in a variety of ways, a phenomenon that may need to be studied further in the context of eliciting such models.
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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 | 0.068 | 0.242 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".