An evaluation of the software architecture efficiency using the Clichés and behavioral diagrams pertaining to the unified modeling language
Bibliographic record
Abstract
The software architecture plays essential role for the development of the complicated software systems and it is important to evaluate the software architecture efficiency. One way to evaluate the software architecture is to create an executable model from the architecture. Unified Modeling Language (UML) diagrams are used to describe the software architecture. UML has made it easy to use and to evaluate the necessary requirements at the software architecture level. It creates an executable model from these diagrams; yet, since the UML is a standard semi-formal language for describing the software architecture, evaluating the software architecture is not directly possible through it. Furthermore, in order to evaluate the software architecture, one needs to turn the actual model into the formal model. In this study, first we describe the architecture using the UML. Then, some properties of the software architecture are mentioned using the UML sequence diagram, deployment diagram, use case diagram, and component diagram. The necessary information associated with the qualitative characteristic of efficiency will be margined as clichs and labels to these diagrams. The independent and dependent components will be extracted from the component diagram. Finally, the resulted semi-formal model will be mapped into a formal model based on the colored Petri net and finally the evaluation will take place.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".