A Semi-automated Decision Support Tool for Requirements Trade-Off Analysis
Bibliographic record
Abstract
System designers and requirements analysts face many competing requirements, such as performance, usability, security, cost, and so forth. To make trade-offs among requirements, ideally analysts would like to quantitatively measure consequences of alternative solutions on requirements. However, during the early stages of requirements and system design, it is hard to quantitatively measure all factors and quantify stakeholders' preferences. The Even Swaps method is a technique developed in management science to assist in multi-criteria decision making which allows the use of available but potentially incomplete quantitative and qualitative measures. It teases out the need to elicit importance weights of requirements. Instead, stakeholders are asked how much they would relax one objective to better achieve another. We apply the Even Swaps technique to requirements trade-offs, and supplement it with an algorithm that automates the decision analysis process. The algorithm fins the most distinguishable pair of alternatives and suggests the next requirements to be swapped to stakeholders.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".