The Firefox temporal defect dataset
Bibliographic record
Abstract
The bug tracking repositories of software projects capture initial defect (bug) reports and the history of interactions among developers, testers, and customers. Extracting and mining information from these repositories is time consuming and daunting. Researchers have focused mostly on analyzing the frequency of the occurrence of defects and their attributes (e.g., The number of comments and lines of code changed, count of developers). However, the counting process eliminates information about the temporal alignment of events leading to changes in the attributes count. Software quality teams could plan and prioritize their work more efficiently if they were aware of these temporal sequences and knew their frequency of occurrence. In this paper, we introduce a novel dataset mined from the Fire fox bug repository (Bugzilla) which contains information about the temporal alignment of developer interactions. Our dataset covers eight years of data from the Fire fox project on activities throughout the project's lifecycle. Some of these activities have not been reported in frequency-based or other temporal datasets. The dataset we mined from the Fire fox project contains new activities, such as reporter experience, file exchange events, code-review process activities, and setting of milestones. We believe that this new dataset will improve analysis of bug reports and enable mining of temporal relationships so that practitioners can enhance their bug-fixing process.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".