Animated Visualization of Software History using Evolution Storyboards
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".