Situational awareness: Personalizing issue tracking systems
Bibliographic record
Abstract
Issue tracking systems play a central role in ongoing software development; they are used by developers to support collaborative bug fixing and the implementation of new features, but they are also used by other stakeholders including managers, QA, and end-users for tasks such as project management, communication and discussion, code reviews, and history tracking. Most such systems are designed around the central metaphor of the “issue” (bug, defect, ticket, feature, etc.), yet increasingly this model seems ill fitted to the practical needs of growing software projects; for example, our analysis of interviews with 20 Mozilla developers who use Bugzilla heavily revealed that developers face challenges maintaining a global understanding of the issues they are involved with, and that they desire improved support for situational awareness that is difficult to achieve with current issue management systems. In this paper we motivate the need for personalized issue tracking that is centered around the information needs of individual developers together with improved logistical support for the tasks they perform. We also describe an initial approach to implement such a system - extending Bugzilla - that enhances a developer's situational awareness of their working context by providing views that are tailored to specific tasks they frequently perform; we are actively improving this prototype with input from Mozilla developers.
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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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".