A Framework to Communicate Software Engineering Data Effectively with Dashboards
Bibliographic record
Abstract
Different approaches have been explored to capture Software Engineering (SE) data and to understand which indica-tors or metrics are essential to be observed in this process. How-ever, the presentation of this data using Information Visualization (InfoVis) systems, such as Dashboards, must be carried out more effectively. Dashboard users often face challenges interpreting the essence of the presented information. Moreover, keeping this audience engaged and leading them to act is still an open topic for investigation. Hence, my research investigates how SE data can be communicated to inform and inspire meaningful actions in a software development organization. The expected contributions are threefold: (1) an overview of the current state of how SE data is communicated across the industry; (2) an exploration of Info Vis approaches combined with a set of practices that can be extended for different applications; and (3) a theoretical framework to guide how SE data can be effectively communicated using Dashboards.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".