Bibliographic record
Abstract
Aspect-oriented (A-O) programming has emerged as a promising paradigm to improve modularity by providing mechanisms to capture and execute crosscutting concerns in software applications. Among others, A-O allows developers to incrementally modifies the behavior of a base program, by introducing aspects which implement crosscutting concerns having effects at various points throughout a program. Hence, despite the clean separation of concerns in aspect-oriented systems, it remains difficult to predict the effect of a given aspect on this base program. Once woven, does an aspect still achieve what it was intended for? Does it violate base program properties that should be preserved? Does it interfere with the properties of other aspects? We propose to address these questions through the formal analysis and verification of A-O system model. More precisely, this work considers A-O models written in Aspect-UML (our UML profile). Having no regards to A-O language specific features, these models might just as well be the result of a forward as of a backward engineering process. In particular, this article explains how Aspect-UML models can be specified within the Alloy model analyzer and how aspect interactions can therefore be verified.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".