Animated Visualization of Software History using Evolution Storyboards
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The understanding of the structure of a software system can be improved by analyzing the system's evolution during development. Visualizations of software history that provide only static views do not capture the dynamic nature of software evolution. We present a new visualization technique, the Evolution Storyboard, which provides dynamic views of the evolution of a software's structure. An evolution storyboard consists of a sequence of animated panels, which highlight the structural changes in the system; one panel for each considered time period. Using storyboards, engineers can spot good design, signs of structural decay, or the spread of cross cutting concerns in the code. We implemented our concepts in a tool, which automatically extracts software dependency graphs from version control repositories and computes storyboards based on panels for different time periods. For applying our approach in practice, we provide a step by step guide that others can follow along the storyboard visualizations, in order to study the evolution of large systems. We have applied our method to several large open source software systems. In this paper, we demonstrate that our method provides additional information (compared to static views) on the ArgoUML project, an open source UML modeling tool
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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 | 0.000 |
| 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 | 0.000 | 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 it