Using Model Transformation Language Semantics for Aspects Composition
Bibliographic record
Abstract
Modern software systems are huge, complex, and greatly distributed.In order to design and model such systems, software architects are faced with the problem of cross-cutting concerns much earlier in the development process.At this level, cross-cutting concerns result in model elements that cross-cut the structural and behavioral views of the system.Research has shown that Aspect Oriented (AO) techniques can be applied to software design models.This can greatly help software architects and developers to isolate, reason, express, conceptualize, and work with cross-cutting concerns separately from the core functionality (Ajila et al., 2010;Petriu et al, 2007).This application of AO techniques much earlier in the development process has spawned a new field of study called Aspect-Oriented Modeling (AOM).In AOM, the aspect that encapsulates the cross-cutting behavior or structure is a model, just like the base system model it cross-cuts.A system been modeled has several views including structural and behavioral views.Therefore, a definition of an aspect depends on the view of interest.Unified Modeling Language (UML) provides different diagrams to describe the different views.Class, Object, Composite Structure, Component, Package, and Deployment diagrams can be used to represent the structural view of a system or aspect.On the other hand, Activity, State Machine, and Interaction diagrams are used to model the behavioral view.Interaction diagrams include Sequence, Interaction Overview, Communication, and Timing diagrams.After reasoning and working with aspects in isolation, the aspect models eventually have to be combined with the base system model to produce an integrated system model.This merging of the aspect model with the base model is called Aspect Composition or Weaving.Several approaches have been proposed for aspect composition using different technologies/methodologies such as graph transformations (Wittle & Jayaraman, 2007), matching and merging of model elements (Fleury et al., 2007), weaving models (Didonet et al., 2006) and others.The goal of this research is to compose aspect models represented as UML sequence diagrams using transformation models written in Atlas Transformation Language (ATL).Composing behavioral models (views) represented as UML Sequence diagrams is more complex than composing structural views.Not only is the relationships between the www.intechopen.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".