Bibliographic record
Abstract
Software product lines gain momentum in research and industry. Many product-line approaches use features as a central abstraction mechanism. Feature-oriented software development aims at encapsulating features in cohesive units to support program comprehension, variability, and reuse. Surprisingly, not much is known about the characteristics of cohesion in feature-oriented product lines, although proper cohesion is of special interest in product-line engineering due to its focus on variability and reuse. To fill this gap, we conduct an exploratory study on forty software product lines of different sizes and domains. A distinguishing property of our approach is that we use both classic software measures and novel measures that are based on distances in clustering layouts, which can be used also for visual exploration of product-line architectures. This way, we can draw a holistic picture of feature cohesion. In our exploratory study, we found several interesting correlations (e.g., between development process and feature cohesion) and we discuss insights and perspectives of investigating feature cohesion (e.g., regarding feature interfaces and programming style).
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".