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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".