Bibliographic record
Abstract
Product line engineering is a concept that has emerged in the 80's in the business schools and is now among the hottest topics in software engineering.Software product lines aim at achieving scope economies through synergetic development of software products. Diverse benefits like cost reduction, decreased time-to-market, and quality improvement can be expected from reuse of domain-specific software assets. But also non-technical benefits can be expected as result of network externalities, product branding, and sharing organizational costs.Product lines introduce additional complexity. In a sense they go against the common adage of "divide and conquer." Planning and/or developing of more than one product at a time have to be managed technically and organizationally.However, the rate of innovation of the technology and the intrinsic nature of software products do not let alternatives to developers: users like to jump into the bandwagon of new products, and old products often drive preferences to new products.Research has been conducted in software product lines for the past few years. Some of it has focused on demonstrating that existing systems and approaches were indeed instrumental for product line development, such as generative techniques, domain analysis and engineering and software components. Another portion of the research effort has tried to determine how it is possible to create a comprehensive methodology and an associated tool for product lines, starting from the business idea of line of products down to the development of a product and trying to exploit all the possible synergies existing at each phase, from network externalities to component reuse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".