How should we read and analyze bug reports: an interactive visualization using extractive summaries and topic evolution
Bibliographic record
Abstract
Software projects evolve over time as bugs are addressed and new functionalities are added. Managing bugs can be a significant challenge for a project manager especially when the number of reported bugs is large, and the manager needs to consult with them. It is also preferable that developers new to a project first familiarize themselves with the project and the reported bugs before actually working on them. In order to reduce developers' time and efforts for reading a bug report, in this paper, we propose a visualization technique that provides an extractive summary visualization for a given bug report. In addition, our proposed technique assists the developers or managers in reviewing a project's bug reports by interactively visualizing insightful information using topic analysis on the bug reports. In order to validate the effectiveness of our proposed visualization technique, we conducted a task-oriented user study involving six participants and a case study using 3914 bug reports. The findings from both studies show that our visualization technique is promising, and it can assist the comprehension and analysis of bug reports. The results from the user study indicate that visualized summary is relatively preferred to the non-visualized summary for quick comprehension of bug reports.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".