GENERALIZATION AND INSTANTIATION FOR COMPONENT REUSE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is an increasing need for high-quality software components. Reusable components and formal specifications are two complementary and promising approaches to achieve this goal. One method for enhancing the reusability of existing components is generalization that creates generic components by parameterizing specific ones. Generalization and instantiation are two methods related respectively to the development for reuse and development with reuse. Generalization, that is the abstraction of existing components, identifies commonalities across a class of entities, while instantiation customizes the general properties under different circumstances. In this paper, we present several generalization and instantiation algorithms for algebraic specifications. A major difficulty during the generalization process is determining the appropriate level of generality. Highly specific components have little chance of being reused. Meanwhile, if a component is too general, its reuse might also be hard. Therefore, we introduce a novel method based on the categorized constructors to control the level of abstraction in generic components with the goal of producing effective reusable components. Through a medium-scale example, the generalization and instantiation operations are illustrated in detail.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it