Bibliographic record
Abstract
Defining, prioritizing, and selecting requirements are problems of tremendous importance. In this paper, a new approach called Quantitative WinWin for decision support in requirements negotiation is studied. The difference to Boehm's WinWin groupware-based negotiation support is the inclusion of quantitative methods as a backbone for better and more objective decisions. Like Boehm's original WinWin, Quantitative WinWin uses an iterative approach, with the aim to increase knowledge about the requirements during each iteration. The novelty of the presented idea is three-fold. Firstly, it uses the Analytical Hierarchy Process for a stepwise determination of the stakeholders' preferences in quantitative terms. Secondly, these results are combined with methods for early effort estimation, in our case using the simulation prototype GENSIM, to evaluate the feasibility of alternative requirements subsets in terms of their related implementation efforts. Thirdly, it reflects the increasing knowledge gained about the requirements during each iteration, in a similar way as it is done in Boehm's spiral model for software development. As main result, quantitative WinWin offers decision support for selecting the most appropriate requirements based on the preferences of the stakeholders, the business value of requirements and a given maximum development effort.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".