Dynamic Software Product Line Engineering: A Reference Framework
Bibliographic record
Abstract
Runtime adaptive systems are able to dynamically transform their internal structure, and hence their behavior, in response to internal or external changes. Such transformations provide the basis for new functionalities or improvements of the non-functional properties that match operational requirements and standards. Software Product Line Engineering (SPLE) has introduced several models and mechanisms for variability modeling and management. Dynamic software product lines (DSPL) engineering exploits the knowledge acquired in SPLE to develop systems that can be context-aware, post-deployment reconfigurable, or runtime adaptive. This paper focuses on DSPL engineering approaches for developing runtime adaptive systems and proposes a framework for classifying and comparing these approaches from two distinct perspectives: adaptation properties and adaptation realization. These two perspectives are linked together by a series of guidelines that help to select a suitable adaptation realization approach based on desired adaptation types.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".