Bibliographic record
Abstract
Abstract Reuse of products, processes, and experience originating from the system life cycle is seen today as a feasible solution to the problem of developing higher quality systems at a lower cost. In fact, quality improvement is very often achieved by repeatedly reusing and modifying the same elements, learning about them by direct experience. This article presents an infrastructure, called the experience factory , aimed at capitalization and reuse of life‐cycle experience and products. The experience factory is a logical and physical organization, and its activities are independent from those of the development organization. The activities of the development organization and of the experience factory can be summarized as follows: The development organization develops and delivers systems with the aid of analyzed, synthesized, and packaged experiences from the experience factory. It provides the experience factory with raw project information such as developmental and environmental characteristics, product parts, processes, and resource and defect data, representing the project being developed. The experience factory supports project developments with direct feedback by analyzing and synthesizing all kinds of experiences gathered from projects as well as other state‐of‐the‐practice notions and acting as a repository for such experiences. These experiences include locally calibrated cost estimation models, processes demonstrated effective for the development environment, relevant products and product parts, and quality models.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.026 |
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".