Bibliographic record
Abstract
Agile software development (ASD) and software product line engineering (SPLE) seem to be two rewarding yet disparate schools of thoughts in software engineering. ASD encourages strong business involvement in development activities, focuses only on the requirements at hand, and deems huge investment in requirement and design upfront unjustifiable. On the other hand, SPLE considers intensive domain analysis and flexible & detailed software design as prerequisites to any development effort. SPLE plans for potential future projects, and dedicates considerable resources for preplanning efforts. Integrating ASD and SPLE, although is challenging, has a huge potential of magnifying enhancements in quality, cuts in cost and reductions in time-to-market. In this paper, we present our research on this integration. We propose a model that enables agile organizations to establish product lines without disturbing the agility of their practices. The model is a bottom-up application-driven approach that relies on automated tests to derive core assets from existing code. 1.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".