Product Line Stakeholder Preference Elicitation via Decision Processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the software product line configuration process, certain features are selected based on the stakeholders' needs and preferences regarding the available functional and quality properties. This book chapter presents how a product configuration can be modeled as a decision process and how an optimal strategy representing the stakeholders' desirable configuration can be found. In the decision process model of product configuration, the product is configured by making decisions at a number of decision points. The decisions at each of these decision points contribute to functional and quality attributes of the final product. In order to find an optimal strategy for the decision process, a utility-based approach can be adopted, through which, the strategy with the highest utility is selected as the optimal strategy. In order to define utility for each strategy, a multi-attribute utility function is defined over functional and quality properties of a configured product and a utility elicitation process is then introduced for finding this utility function. The utility elicitation process works based on asking gamble queries over functional and quality requirement from the stakeholder. Using this utility function, the optimal strategy and therefore optimal product configuration is determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| 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 | 0.002 |
| 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