ERC – An object-oriented refinement calculus for Eiffel
Bibliographic record
Abstract
Abstract. We present a refinement calculus for transforming object-oriented (OO) specifications (or ‘contracts’) of classes into executable Eiffel programs. The calculus includes the usual collection of algorithmic refinement rules for assignments, if-statements, and loops. However, the calculus also deals with some of the specific challenges of OO, namely rules for introducing feature calls and reference types (involving aliasing). The refinement process is compositional in the sense that a class specification is refined to code based only on the specifications (not the implementations) of the classes that the specification depends upon. We discuss how automated support for such a process can be developed based on existing tools. This work is done in the context of a larger project involving methods for the seamless design of OO software in the graphical design notation BON (akin to UML). The goal is to maintain model and source code integrity, i.e., the software developer can work on either the model or the code, where (ideally) changes in one view are reflected instantaneously and automatically in all views.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".