Architecture Based Development with DYNACOMM: Incorporating Dynamic Reconfiguration and Hierarchical Design into CommUnity
Bibliographic record
Abstract
Architecture Description Languages (ADLs) were developed to support the abstract level of software structuring that is the subject matter of software architecture. Community is an ADL built on co-ordination principles and a categorical framework to support the composition of specifications of components to form the system's specification. However, an important shortcoming of Community is the lack of integrated support for specifying the system's architectural changes in both the set of components and the connections between them. This paper presents DynaComm, an extension of Community to support hierarchical design and dynamic reconfiguration of component based systems. The architectural design principles supported by the DynaComm language are illustrated through the design of a fault-tolerant, dynamic client-server system. A state anchored semantics of DynaComm is given by reducing a DynaComm design to a Community design, i.e., a configuration of components, cables and superposition morphisms, and then taking the colimit of the Community diagram. The careful use of extension, refinement and superposition morphisms guarantees that this is always possible.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.005 |
| 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".