From Products to Product Lines: a Model-based Methodology
Bibliographic record
Abstract
To effectively manage collections of system and software products, industries are increasingly relying on model-based solutions and product lines (PLs). PLs enable developing, analyzing, and maintaining multiple related products. However, adopting PLs requires tool developers and users to extend existing model management (MoM) frameworks so that they are able to support the lifecycle management of PL models. In this paper, we describe a methodology for doing so. Our methodology identifies four key model management components required to define, transform, and evolve PL models, and relates them to each other and to traditional MoM components. To support rigorous reasoning about MoM of PLs, we combine existing techniques for PL modelling, PL modelling operators, and relations between product and PL models into a unified formal framework. We present an exemplar application of our methodology which extends a product-level modelling tool to support MoM for a product line of medical devices.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".