Non-functional Properties in Software Product Lines: A Taxonomy for Classification.
Bibliographic record
Abstract
In the recent years, the software product lines paradigm has gained interest in both industry and academia. As in traditional software development, the concept of quality is crucial for the success of software product line practices and both functional and nonfunctional characteristics must be involved in the development process in order to achieve a high quality software product line. Therefore, many efforts have been made towards the development of quality-based approaches in order to address non-functional properties in software product line development. In this paper, we propose a taxonomy that characterizes and classifies various approaches for employing non-functional properties in software product lines development. The taxonomy not only highlights the major concerns that need to be addressed in the area of quality-based software product lines, but also helps to identify various research gaps that need to be filled in future work in this area.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".