On the Difficulties of Raising the Level of Abstraction and Facilitating Reuse in Software Modelling: The Case for Signature Extension
Bibliographic record
Abstract
Reuse is central to improving the software development process, increasing software quality and decreasing time-to-market. Hence it is of paramount importance that modelling languages provide features that enable the specification and modularization of reusable artefacts, as well as their subsequent reuse. In this paper we outline several difficulties caused by the finality of method signatures that make it hard to specify and use reusable artefacts encapsulating several variants. The difficulties are illustrated with a running example. To evaluate whether these difficulties can be observed at the programming level, we report on an empirical study conducted on the Java Platform API as well as present workarounds used in various programming languages to deal with the rigid nature of signatures. Finally, we outline signature extension as an approach to overcome these problems at the modelling level.
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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 | 0.088 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.010 | 0.031 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.012 |
| 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".