Slicing uml's three-layer architecture: a semantic foundation for behavioural specification
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. Our research focuses on a top-to-bottom slice of the three-layer architecture. We formally define the execution semantics of two-thirds of UML actions, including the most complicated actions---invocation actions. Our formal definition is expressed in terms of state changes to a global state machine representing an executing UML model. Our work provides an alternate formalization to that of the current submission to the RFP and could be used to enhance that submission. To validate our formal semantics and to determine the usefulness of the three-layer architecture, we have created an interpreter for UML actions and activities. This interpreter was designed in accordance with the complex token passing semantics of UML and provides analysis capabilities that have been successfully used to identify problems even in published activity diagrams. In effect, we have created a tool that supports the construction, verification and execution of a subset of UML models, namely activities. Our handling of this slice of the three-layer architecture is a preliminary step to realizing the grander vision of general executable (and analyzable) models.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".