Bibliographic record
Abstract
Most open source software development projects include an open bug repository---one to which users of the software can gain full access---that is used to report and track problems with, and potential enhancements to, the software system. There are several potential advantages to the use of an open bug repository: more problems with the system might be identified because of the relative ease of reporting bugs, more problems might be fixed because more developers might engage in problem solving, and developers and users can engage in focused conversations about the bugs, allowing users input into the direction of the system. However, there are also some potential disadvantages such as the possibility that developers must process irrelevant bugs that reduce their productivity. Despite the rise in use of open bug repositories, there is little data about what is stored inside these repositories and how they are used. In this paper, we provide an initial characterization of two open bug repositories from the Eclipse and Firefox projects, describe the duplicate bug and bug triage problems that arise with these open bug repositories, and discuss how we are applying machine learning technology to help automate these processes.
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 | 0.023 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| 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".