Component Comparison, Evaluation, and Selection: A Continuous Approach
Bibliographic record
Abstract
Early visions of component-based software development have been realized, with software projects now composed mostly of other peoples code. However, the challenge of selecting the best components, with speed and confidence in the result, has only become more difficult. Previous work has focused on systematic approaches to component selection, but in continuous-*and agile settings, the increase in confidence from being systematic is not worth the cost of delay. In this emerging ideas paper, we present early results on work to balance speed with confidence in component selection. Our idea is to define a scorecard for components based on high-level quality attribute indicators, project health measures, and a context-specific aggregation function for producing a single yes/no decision for integrators. We present preliminary results showing how this scorecard approach works on computer vision components.
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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 | 0.116 | 0.272 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".