Boxology of NBA and TA: A Basis for Understanding Software Architecture
Bibliographic record
Abstract
Box-and-arrow diagrams seem inevitable for presentation of software architecture; however, the term "boxology" often mocks their over-use, especially when informal. We introduce in this paper a formal boxology to serve as a semantic domain for graph-based software architecture representation languages: the nested boxes and arrows (NBA) model. NBA graphs use commonly-adopted features of structure diagrams for software: boxes for objects, arrows for relations, attributes for values, and a containment hierarchy. NBA graphs are visualized using a number of conventions, and are transmitted in exchange languages such as GXL and TA. The NBA model is formalized as typed graphs with attributes and an identified spanning tree (containment). Meta-modeling is defined and formalized by schemas, which are also NBA graphs. The universal schema is defined. A number of tools have been developed to query, manipulate and visualize NBA graphs
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".