A Formal Engineering Approach to Product Family Modeling
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
Software Product Line deals with the development of product families for diverse market needs and includes feature model to describe the structure of the included products. Since feature model is lack of detailed specification of individual features, some behavior-oriented methods have been proposed to analyze the inner functionalities of features. But how these functions relate to the feature model remains a problem and a systematic approach is still needed to support the whole process of product family modeling. This paper provides a formal engineering approach to modeling product family where feature model evolves as individual features are formalized through informal, semi-formal and formal stages. For each stage, a set of evolvement rules are given to guide the refactoring of the feature model which will then serve as a basis for formal specifications of individual features. Such an iterative process repeats until achieving a feature model with consistent feature specifications. A case study is described to illustrate the effectiveness of our approach.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 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