How do flexible processes relate to software product-lines
Bibliographic record
Abstract
YIP Jason (1), SUCCI Giancarlo (2), LIU Eric (1)(1) Department of Electrical and Computer Engineering, University of Calgary, Calgary,AB, Canada. {jchyip, liue}@ucalgary.ca(2) Department of Electrical and Computer Engineering, University of Alberta,Edmonton, AB, Canada. Giancarlo.Succi@ee.ualberta.caKeywords: flexible processes, software product linesAbstractFlexible processes and software product lines are two different proposals to improvesoftware development. There is a tendency for software product lines to be associatedwith building reusable frameworks, which may not fit well with a flexible process.Software product lines are not solely concerned with reusable frameworks and in fact,many aspects can be used in a flexible process. The two approaches can learn from eachother to further improve the effectiveness of software development.1 IntroductionFlexible processes and software product lines are two different proposals to improvingthe productivity and effectiveness of software development.Flexible processes are “value-centric”, meaning that the focus is on delivering businessvalue early with evolutionary delivery [Gilb97] instead of extensive up-front design.Some other defining aspects of flexible processes include reduced artifacts and anemphasis on informal face-to-face communication. Flexible processes promise toimprove productivity by reducing overhead.J. Yip, G. Succi, E. Liu (June 2000) How do Flexible Processes Relate to Software Product-Lines? Proceedings of the First International Conference on Extreme Programming and Flexible Processes in Software Engineering (XP2000), Cagliari, Italy
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".