Trade-off Analysis for Requirements Selection
Bibliographic record
Abstract
Evaluation, prioritization and selection of candidate requirements are of tremendous importance and impact for subsequent software development. Effort, time as well as quality constraints have to be taken into account. Typically, different stakeholders have conflicting priorities and the requirements of all these stakeholders have to be balanced in an appropriate way to ensure maximum value of the final set of requirements. Trade-off analysis is needed to proactively explore the impact of certain decisions in terms of all the criteria and constraints. The proposed method called Quantitative WinWin uses an evolutionary approach to provide support for requirements negotiations. The novelty of the presented idea is four-fold. Firstly, it iteratively uses the Analytical Hierarchy Process (AHP) for a stepwise analysis with the aim to balance the stakeholders' preferences related to different classes of requirements. Secondly, requirements selection is based on predicting and rebalancing its impact on effort, time and quality. Both prediction and rebalancing uses the simulation model prototype GENSIM. Thirdly, alternative solution sets offered for decision-making are developed incrementally based on thresholds for the degree of importance of requirements and heuristics to find a best fit to constraints. Finally, trade-off analysis is used to determine non-dominated extensions of the maximum value that is achievable under resource and quality constraints. As a main result, quantitative WinWin proposes a small number of possible sets of requirements from which the actual decision-maker can finally select the most appropriate solution.
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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 | 0.023 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".