Fused data-centric visualizations for software evolution environments
Bibliographic record
Abstract
During software evolution, several different facets of the system need to be related to one another at multiple levels of abstraction. Current software evolution tools have limited capabilities for effectively visualizing and evolving multiple system facets in an integrated manner. Many tools provide methods for tracking and relating different levels of abstraction within a single facet. However, it is less well understood how to represent and understand relationships between and among different abstraction hierarchies, i.e. for inter-hierarchy relations. Often, these are represented and explored independently, making them difficult to relate to one another. As a result, engineers are likely to have difficulty understanding how the various facets of a system relate and interact. We describe preliminary results of a collaborative research project between industry and academia to enhance the inter-hierarchy visualization capabilities of an existing software evolution environment called "KLOCwork Suite". Specifically, we describe our efforts to add a "fused" visualization based on story board diagrams. This visualization integrates - or "fuses" - facets of architecture, behavior and data. We describe how these diagrams bridge currently isolated visualizations of system information, and argue how they can help drive architecture excavation tasks.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".