Unifying Software and Product Configuration: A Research Roadmap
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
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. 1
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.000 | 0.004 |
| 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