Impact of switching bug trackers: a case study on a medium-sized open source project
Bibliographic record
Abstract
For most software projects, the bug tracker is an essential tool. In open source development, this tool plays an even more central role as it is generally open to all users, who are encouraged to test the software and report bugs. Previous studies have highlighted the act of reporting a bug as a first step leading a user to become an active contributor. The impact of the bug reporting environment on the bug tracking activity is difficult to assess because of the lack of comparison points. In this paper, we take advantage of the switch, from Bugzilla to GitHub, of the bug tracker of Coq, a medium-sized open source project, to evaluate and interpret the impact that such a change can have. We first report on the switch itself, including the migration of preexisting issues. Then we analyze data from before and after the switch using a regression discontinuity design, an econometric methodology imported from quantitative policy analysis. We complete this quantitative analysis with qualitative data from interviews with developers. We show that the switch induces an increase in bug reporting, particularly from principal developers themselves, and more generally an increased engagement with the bug tracking platform, with more comments by developers and also more external commentators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".