Ensuring structure and behavior correctness in design composition
Bibliographic record
Abstract
The design of a large component-based software system typically involves the composition of different components. Instead of relying on a free composition of components, we advocate that more rigorous analysis methods to check the correctness of component composition would allow combination problems to be detected early in the development process so that people can save considerable effort offering errors downstream. In this paper we describe a rigorous method for component composition that can be used to solve combination and integration problems at the (architectural) design phase of the software development lifecycle. In addition, we introduce the notion of composition pattern in order to promote the reuse of composition solutions to solve routine component composition problems. Once a composition pattern is proven correct, its instances can be used in a particular application without further proof. In this way, our proposed method involves reusing compositions as well as reusing components. We illustrate the utility of our approach through an example related to the composition of design patterns as design components. Structural and behavioral correctness proofs about the composition of some design patterns are provided.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".