Requirements trade-offs analysis in the absence of quantitative measures
Why this work is in the frame
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Bibliographic record
Abstract
Simultaneously satisfying multiple interacting and possibly conflicting software requirements is challenging. Quantitative cost-benefit analysis of alternative solutions is often hard or biased, and early decisions based on numerical estimates of requirements satisfaction are thus unreliable. We propose a trade-off analysis method that assists decision making in the absence of numerical data. We structure the requirements trade-off problem in terms of a goal model containing alternative design solutions and decision criteria. We propose a trade-off analysis algorithm that takes pair-wise comparisons of alternatives and determines the best solution among alternatives. The optimum alternative is decided by using a heuristic method, which may need to consult with domain experts. We take advantage of the Even Swaps method [1] to incorporate stakeholders' preferences into the decision analysis. The algorithm is implemented in a prototype tool and evaluated in an industrial case study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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