Developer Dashboards: The Need for Qualitative Analytics
Bibliographic record
Abstract
Prominent technology companies including IBM, Microsoft, and Google have embraced an analytics-driven culture to help improve their decision making. Analytics aim to help practitioners answer questions critical to their projects, such as "Are we on track to deliver the next release on schedule?" and "Of the recent features added, which are the most prone to defects?" by providing fact-based views about projects. Analytic results are often quantitative in nature, presenting data as graphical dashboards with reports and charts. Although current dashboards are often geared toward project managers, they aren't well suited to help individual developers. Mozilla developer interviews show that developers face challenges maintaining a global understanding of the tasks they're working on and that they desire improved support for situational awareness, a form of qualitative analytics that's difficult to achieve with current quantitative tools. This article motivates the need for qualitative dashboards designed to improve developers' situational awareness by providing task tracking and prioritizing capabilities, presenting insights on the workloads of others, listing individual actions, and providing custom views to help manage workload while performing day-to-day development tasks.
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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 | 0.184 | 0.333 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".