Slicing uml's three-layer architecture: a semantic foundation for behavioural specification
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
One of the main notational contexts in which model-driven software development has been studied is the Unified Modeling Language (UML), the de facto standard in software modelling.The current trend in software development is not just towards the use of models, but the use of executable models.In 2006, the Object Management Group issued a Request for Proposal (RFP), soliciting the definition of an Executable UML Foundation, with a fully specified executable semantics.The purpose of such a version of UML is to make the advantages of executable models available to UML users by enabling "a chain of tools that support the construction, verification, translation, and execution" of models.An oft-voiced criticism of UML is its lack of a formal, unambiguous description of its semantics.In an effort to improve the support for model-driven development, especially with respect to executable modelling, the UML 2 specification introduced a novel three-layer semantics architecture.This architecture provides a stratification of the description of UML models that clearly separates 'low-level' behavioural specification mechanisms, such as actions, from 'high-level' behavioural formalisms, such as activities, state machines and interactions.Although UML describes the effect of actions, it does not provide either the concrete syntax or the formal semantics of an action language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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