Unifying Software and Product Configuration: A Research Roadmap
Bibliographic record
Abstract
For more than 30 years, knowledge-based product configuration systems have been successfully applied in many industrial domains. Correspondingly, a large number of advanced techniques and algorithms have been developed in academia and industry to support different aspects of configuration reasoning. While traditional research in the field focused on the configuration of physical artefacts, recognition of the business value of customizable software products led to the emergence of software product line engineering. Despite the significant overlap in research interests, the two fields mainly evolved in isolation. Only limited attempts were made at combining the approaches developed in the different fields. In this paper, we first aim to give an overview of commonalities and differences between software product line engineering and product configuration. We then identify opportunities for cross-fertilization between these fields and finally develop a research agenda to combine their respective techniques. Ultimately, this should lead to a unified configuration approach.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.029 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.020 | 0.067 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".