Cool features and tough decisions
Bibliographic record
Abstract
Variability modeling is essential for defining and managing the commonalities and variabilities in software product lines. Numerous variability modeling approaches exist today to support domain and application engineering activities. Most are based on feature modeling (FM) or decision modeling (DM), but so far no systematic comparison exists between these two classes of approaches. Over the last two decades many new features have been added to both FM and DM and it is tough to decide which approach to use for what purpose. This paper clarifies the relation between FM and DM. We aim to systematize the research field of variability modeling and to explore potential synergies. We compare multiple aspects of FM and DM ranging from historical origins and rationale, through syntactic and semantic richness, to tool support, identifying commonalities and differences. We hope that this effort will improve the understanding of the range of approaches to variability modeling by discussing the possible variations. This will provide insights to users considering adopting variability modeling in practice and to designers of new languages, such as the new OMG Common Variability Language.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".