Integration de la visualisation a multiples vues pour le developpement du logiciel
Bibliographic record
Abstract
Nowadays, software development has to deal more and more with huge complex programs, constructed and maintained by large teams working in different locations. During their daily tasks, each developer may have to answer varied questions using information coming from different sources. In order to improve global performance during software development, we propose to integrate into a popular integrated development environment (Eclipse ) our new visualization tool (VERSO), which computes, organizes, displays and allows navigation through information in a coherent, effective, and intuitive way in order to benefit from the human visual system when exploring complex data. We propose to structure information along three axes: (1) context (quality, version control, etc.) determines the type of information; (2) granularity level (code line, method, class, and package) determines the appropriate level of detail; and (3) evolution extracts information from the desired software version. Each software view corresponds to a discrete coordinate according to these three axes. Coherence is maintained by navigating only between adjacent views, which reduces cognitive effort as users search information to answer their questions. Two experiments involving representative tasks have validated the utility of our integrated approach. The results lead us to believe that an access to varied information represented graphically and coherently should be highly beneficial to the development of modern software. Keywords: Visualization, software development, development environment, integration, software evolution, animation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".